{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "799a6f9c",
   "metadata": {},
   "source": [
    "## 1. 数据预处理与特征工程\n",
    "\n",
    "- 读取ASD和TD文件夹下所有csv文件，每个csv为一个样本。\n",
    "- 每个样本只取前2000帧，保留Gaze_X、Gaze_Y、Expression三列。\n",
    "- 展平成一维特征向量，长度不足补零。\n",
    "- 合并所有样本，准备标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83c0681f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本总数: 269, ASD: 124, TD: 145\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "ASD_DIR = 'ASD'\n",
    "TD_DIR = 'TD'\n",
    "FRAME_LIMIT = 2000\n",
    "\n",
    "def load_samples(folder, label):\n",
    "    samples = []\n",
    "    for fname in os.listdir(folder):\n",
    "        if fname.endswith('.csv'):\n",
    "            fpath = os.path.join(folder, fname)\n",
    "            df = pd.read_csv(fpath)\n",
    "            df = df.head(FRAME_LIMIT)\n",
    "            df = df[['Gaze_X', 'Gaze_Y', 'Expression']]\n",
    "            feature = df.values.flatten()\n",
    "            samples.append((feature, label))\n",
    "    return samples\n",
    "\n",
    "asd_samples = load_samples(ASD_DIR, 1)\n",
    "td_samples = load_samples(TD_DIR, 0)\n",
    "\n",
    "all_samples = asd_samples + td_samples\n",
    "X = [s[0] for s in all_samples]\n",
    "y = [s[1] for s in all_samples]\n",
    "\n",
    "max_len = FRAME_LIMIT * 3\n",
    "X = [np.pad(x, (0, max_len - len(x)), 'constant') for x in X]\n",
    "X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "print(f'样本总数: {len(X)}, ASD: {sum(y==1)}, TD: {sum(y==0)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5752d6",
   "metadata": {},
   "source": [
    "## 2. 划分训练集与测试集\n",
    "\n",
    "- 按样本划分，80%训练集，20%测试集。\n",
    "- 使用stratify参数，保证ASD和TD比例均衡。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3378d87b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集样本数: 215, 测试集样本数: 54\n",
      "训练集ASD比例: 0.46, 测试集ASD比例: 0.46\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "print(f'训练集样本数: {len(X_train)}, 测试集样本数: {len(X_test)}')\n",
    "print(f'训练集ASD比例: {np.mean(y_train):.2f}, 测试集ASD比例: {np.mean(y_test):.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a09efa8",
   "metadata": {},
   "source": [
    "## 3. 多种分类算法建模与评估\n",
    "\n",
    "- 选择多种常用分类算法：SVM、随机森林、KNN、逻辑回归、MLP神经网络。\n",
    "- 对每种算法：\n",
    "    1. 用训练集训练模型\n",
    "    2. 用测试集预测并评估准确率、召回率、F1分数\n",
    "    3. 输出混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bbba9196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型: SVM\n",
      "准确率: 0.574, 召回率: 0.200, F1分数: 0.303\n",
      "混淆矩阵:\n",
      "[[26  3]\n",
      " [20  5]]\n",
      "\n",
      "模型: Random Forest\n",
      "准确率: 0.667, 召回率: 0.600, F1分数: 0.625\n",
      "混淆矩阵:\n",
      "[[21  8]\n",
      " [10 15]]\n",
      "\n",
      "模型: KNN\n",
      "准确率: 0.556, 召回率: 0.080, F1分数: 0.143\n",
      "混淆矩阵:\n",
      "[[28  1]\n",
      " [23  2]]\n",
      "\n",
      "模型: Logistic Regression\n",
      "准确率: 0.593, 召回率: 0.240, F1分数: 0.353\n",
      "混淆矩阵:\n",
      "[[26  3]\n",
      " [19  6]]\n",
      "\n",
      "模型: MLP\n",
      "准确率: 0.556, 召回率: 0.040, F1分数: 0.077\n",
      "混淆矩阵:\n",
      "[[29  0]\n",
      " [24  1]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "models = {\n",
    "    'SVM': SVC(),\n",
    "    'Random Forest': RandomForestClassifier(),\n",
    "    'KNN': KNeighborsClassifier(),\n",
    "    'Logistic Regression': LogisticRegression(max_iter=1000),\n",
    "    'MLP': MLPClassifier(max_iter=500)\n",
    "}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    rec = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    print(f'\\n模型: {name}')\n",
    "    print(f'准确率: {acc:.3f}, 召回率: {rec:.3f}, F1分数: {f1:.3f}')\n",
    "    print('混淆矩阵:')\n",
    "    print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612c66e1",
   "metadata": {},
   "source": [
    "## 4. 结果可视化与分析\n",
    "\n",
    "- 绘制各模型的混淆矩阵热力图\n",
    "- 绘制ROC曲线\n",
    "- 分析各模型优劣和误判原因"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "38407b8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\10291\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\10291\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\10291\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\10291\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\10291\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\10291\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\10291\\AppData\\Roaming\\Python\\Python313\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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SBjUy11DdCdLFTxfm/Z8/f4579+6ibNlyn/eUx3S/X2FtHb3hnc7TE3dKl4GlzgcWFs1hbBjMEhGZAAlkHW2N6C3bMiCws7WyhK0xjYsonCSQ1SqY9fb3xrY729TErmcez9Q+RztH1VpLgthkjt9fovnff/9Fy5YtVQ/ZixcvIkOGDGp/TDwnnb8VrD5PPjNGfAciIiIiigUePh7YcGuDarH19tNbtS+xXWK0yN1CBbIJ7RJ+9xh+fn4YPnw4Jk2apLYLFCig9pkzBrNEREREMeit11usvrEaG25ugLuvu9qXyikVWudpjXrZ6sFBWtiFw5MnT1Tv2GPHjqntrl274rfffoO9fdyuWf8eBrNEREREMUBKCJZfXY6td7eq0gKROWFmtM/XHtUzVVf1seG1a9cutZrXu3fvkCBBAixevBgNGzaMwdGbDgazRERERNHo7vu7WHJ1Cf558A/8Py/hnM85nwpiK6arqDoPRJQEsxLIFilSBBs2bEDmzJljYOSmicEsERERUTS49OYSFl9ZjINPDgbuK5mqJDrk64CiKYtGqSPG9OnTkTFjRvTq1Qt2dlwGOigGs0RERERRWIjh+PPjKog9+ypggQILWKByhspqydk8znkiddxt27Zh9erVKgtrZWWl6mIHDBgQzaOPGxjMEhEREUWQv84fex7vwdIrS3Hj3Q21z9rSGrUy10LbvG2RKWGmSB3X29tbBa2zZ89W20uWLEGnTp2idexxDYNZIiIionDy8ffBjns7VI/YR26P1D7pRtAgewO0yt0KKZ1SRvrY9+7dQ+PGjXHu3Dm13a9fP7Rt2zbaxh5XMZglIiIi+o6Pvh+x+fZmrLy2Eq+9Xqt90he2Wc5m6pLIPlGUjr9p0yZ06NABbm5uSJIkCVauXIkaNWpE0+jjNgazRERERKF4/+k91txYg3U318HNx03tS+6YHK1zt1bZWEcbxyg/xsSJEzFkyBD1c+nSpbFu3TqkSxf2Urb0BYNZIiIioq+8/PgSK66twJ93/oSXn5falzFBRrTL2w41MteArZVttD1WzZo1MW7cONWpYMyYMbC2ZngWEXy1iOLYrFov34CehmT6PH38zepv189Hp/UwKBp/n3qvgADQmPgG+RvTeXpC52/1zW0euD1UpQT/PtgNP33AMrE/JMmJNnlao0LaCrCytAK8/aBD1JaQvX3nDrJny6Z+zpMlC25fvoxUqVIBPj7Q+fjAmOiM8HcZFINZojj04dFgwQmce/Re66EQRfhvd8vU83h531XroZAZuVO6DKx0IQeNTT5fvrgGYADuRsPjftLpMPH1K2x1dcWq9BmQ3+HLUrYBRQwUURFfgoKIjJJkZBnIxk1FMiSGg823GaS4QjKyDGQpNiV0vQfLUALZmHTP2xuNHz3CJldXyHmXy5+MO+P5NYdChWARJPg2FszMEsVBZ4dVhqNt3A1+zI0EslFZOciUtJ1SBjZ2/Ns1ZXL6XrKeIsu+PbC0N77gx9KmKA4/S40VV1fg+rvrap+1hTWqZqqm2mtljmSP2LCsWL0a3fv0gaePN1IkT45VS5eiUsWKMCUWDg5G+V7EYJYoDpJA1tGW/3uT6ZFAlsGsaZM6VMPpe7uE8WDpGPXZ/tHF198XO+/vxNKrS/HQ7aHaZ+/ggHrZ6qF1ntZIHS91tD/mx48f0a1bN6xYsUJtV6pUSa3slTJl5PvRUnD8tCMiIqI4zdPXE1vubMGK6ytUlwIR3zY+muZsiua5miOJfZIYe+z169erQNbS0hKjR4/G4MGD1fK0FH0YzBIREVGc5OrtirU312LtjbX44P1B7XN2cFalBA2zN0Q823gxPoZ27drh9OnTaNasGcqXLx/jj2eOGMwSERFRnPLq4yusur4Km25vgqefp9qXLn46tM3bFrWz1IadlV2MPba7uzvGjh2L4cOHI378+KrG9I8//oixxyMGs0RERBRHPHJ7hGVXl2H7ve3w1fmqfTkS50D7fO1RJUMVWFvGbNhz6dIlNGrUCLdv38arV68C62QpZjGYJSIiIpN24+0NLL6yGHse7YEeerWvUPJC6JCvA8qkKRPjM/ClV7JkX3v37g1vb2+kTZsWnTp1itHHpC8YzBIREZHJkQDy7KuzWHJlCY49Pxa4v3za8ioTWzB5wVgZh6urqwpcN27cGLg07fLly5E0adJYeXxiMEtEREQmRKfX4dCTQ1h8dTEuv7ms9llaWOKnjD+pIDZ74uyxNpZr166hTp06uHfvHqytrTF58mT06dPHKHuxxmUMZomIiMjoSQ3s7ge7VY/Yux8CFpa1tbTF/7L9T/WIlQlesc3Z2RkeHh7IkCEDNmzYgOLFi8f6GIjBLBERERmxT36fsPXuViy/uhzPPz5X+5xsnNA4R2O0zN1StdqKTV5eXnD4vKRrihQp8PfffyNTpkxInDhxrI6DvmAwS0REREbHzccNG25uwOobq/Hu0zu1TxY3kAC2UY5GSGCbINbHdOrUKTRu3BiTJk1CkyZN1L5ChQrF+jgoOAazREREZDRcvFyw8vpKbLy1ER99P6p9aeKlQZs8bVA3a13YW9trMtlsxowZGDhwIPz8/FRtrLTgklW9SHsMZomIiEhzT9yfqFKCbXe3wUfno/ZlTZQV7fK2w0+ZfoKNpY0m43r79i3atGmDnTt3qu2GDRti0aJFDGSNCINZIiKKVKZK72cBePsCngErLEWWztv/y8+entD5c916U6bz8orQ7W+9u4UlV5fg34f/qk4FIn+y/KpHbLm05VSnAq0cP35clRM8efIEdnZ2mDlzJjp37sxuBUaGwSwREUU4kH30+zl4PUoFbJYVjqK2ypG/pS1Qbob6+U7pMrD6nJWjuO38q/MqiD389HDgvtJpSqN93vYokqKI5gHjgwcPUL58eVVWkC1bNtVHtkCBApqOiULGYJaIiCJE7+UFr0euWg+DjJxDoUKw+DzrP+gXoSPPjqiFDs6/Pq/2SeZVlpqVIDZX0lwwFtKhoFevXnjx4gUWLFiA+PHjaz0kCgWDWSIiirRsvzWHZcW+UTqGr7c/Dg0+E3C8Y0dhY8cyg7hAAllDdtVP54f/Hv6nMrG3399W+6QGtnaW2mibty0yJMgAY3Do0CEVxKZPn15ty0QvqY3VOktMYWMwS0REkWZpZw1LR8eoHcPqS82sHMuSwWyc4e3vjb/u/oVlV5fhqcdTtc/R2lG11pIWW8kdk8MY+Pv7Y8KECRg1apRa+ECCWhsbG1hZ8W/RFDCYJSIiomjl4eOBjbc3YtX1VarVlkhklwjNczVH05xNkdAuIYzFq1ev0Lx5c+zbt09tZ8+eHb6+viqYJdPAYJaIiIiixVuvt1hzYw3W31wPd193tS+lU0rVI/Z/Wf8HR5uoZfGj2/79+9GsWTMV0Do6OmL+/Plo3bq11sOiCGIwS0RERFHyzOMZVlxbgS13tqjSApEpYSbVI7ZGphqwsTKuLKeUFYwZMwZjx45Vk9Ly5s2LDRs2IHfu3FoPjSKBwSwRERFFyt33d7H06lL8/eBv+OsDap/zJs2resRWTF9R0x6xYZEygm3btqlAtkOHDpg1a5bKzJJpYjBLREREEXLpzSUsvrIYB58cDNxXIlUJFcQWS1nM6Gf/29vbq76x586dU2UGZNoYzBIREdF3SRbz+PPjqr3WmZcBrdQsYIHKGSqrcoK8znlhrGThg+HDh8PJyQnDhg1T+3LkyKEuZPoYzBIREVGo/HX+2Pt4r1ro4Ma7G2qftYU1amapqXrEZk6YGcZMlqJt2rQpjh07pnrGNm7cWK3oRXEHg1kiIiL6ho+/D3bc24Fl15bhkdsjtc/B2gH1s9VH6zytVZcCY7dr1y60atUK7969Q4IECbBo0SIGsnEQg1kiIiIK5OnriU23N2HltZV47fVa7UtgmwDNcjVDs5zNkNg+MYydTPAaMmQIpk2bprYLFy6suhVkyZJF66FRDGAwS0RERPjw6QPW3FyDtTfWws3HTe1L7pAcrfK0QoPsDeBk4wRTqe2tVq0aDhw4oLZ79uyJKVOmwM7OTuuhUQxhMEtERGTGXn58qXrE/nnnT3j5eal9GRJkQNs8bVErSy3YWtnClEgnBamLvXDhApYuXYr//e9/Wg+JYhiDWSIiIjP0wPWB6hG78/5O+On81L5cSXKhfb72qJy+MqwsrWAqvL298fTp08Aygk6dOqFu3bpIkSKF1kOjWMBgloiIIkwPQGdpC18/S1h6BzTLjyzfKN6fIuaayzXVXmvvo73Qq98kUDRlUbTP2x6lUpcy+h6xX7t//z4aNWoEFxcXlY1NnDixeg4MZM0Hg1kiIopwTeL5gn3hmjALDm0DsO2Q1kOicPzOTr88rRY6OPniZOD+CukqqIUO8ifLD1O0efNmtG/fHm5ubkiSJAlu376N4sWLaz0simUMZomIKEL8fHQqkI1uqbIkhLWtcS5/aqp0eh0OPD6gMrFXXK6ofVYWVvg5089qoYOsibPCFH369Am//vor5s+fr7ZLly6NdevWIV26dFoPjTTAYJaIiCKtdc3zsKvSJ1qOJYGsqZ3iNla+Ol/sur9L1cRKbayws7JDvWz1VI/YNPHSwFTduXNHlRVcvHhRbQ8aNAhjxoyBjY2N1kMjjTCYJSKiSLOx1sHGznQmCplDj9itd7di+bXlqkuBiG8TH01yNkHzXM2R1CEpTN2IESNUIOvs7IxVq1bhp59+0npIpDEGs0RERCbO1dsV626uUz1i33u/V/uS2idVPWIbZW+EeLbxEFfMnTtXZfCnTp2KNGlMN8NM0YfBLBERkYl67fkaq66vwsZbG+Hp56n2pY2XFm3ztkWdrHVUaYGpu3HjBtavX49Ro0apIDZp0qRYu3at1sMiI8JgloiIyMQ8dnus6mG339uu6mNF9sTZVXutqhmrwtoybny8r1y5Er/88gs8PT1VD9lWrVppPSQyQnHjr52IiMgM3Hh7Q3Um2PNoj+pUIAolL6QWOiibpmycmUD38eNHdO/eHcuXL1fbP/74I6pWrar1sMhIMZglIiIy8h6xZ1+dVUHssWfHAveXS1tOZWILpSiEuOTq1auqW4GUF1haWqrygiFDhsDKihMNKWQMZomIiIyQZF4PPz2sFjq49OaS2mdpYYlqGaupIDZHkhyIa6RXrCyC4OXlhVSpUqna2AoVKmg9LDJyDGaJiIiMiJ/OD/88+EfVxN79cFfts7W0Rd2sddEmbxukix93FwZInjy5WhBBSgqk7ZZsE30Pg1kiIiIj8MnvE7bd3aZ6xD7zeKb2Odk4oVGORmiZqyWSOSZDXCT1sU5OTurnSpUq4dChQ2pFLykxIAoPBrNEREQacvdxx4ZbG1SLrXef3ql9SeyToEWuFmicszES2CZAXK0F/uOPP9QiCMePH0fWrAFL65YtW1broZGJYTBLRESkARcvl8AesR6+HmpfaqfUqpRASgocrB0QV7m5uaFjx47YuHGj2pagVhZBIIoMzXP48+bNQ8aMGWFvb4/ixYvj9OnTYd5+5syZyJEjBxwcHJAuXTr06dNH1dcQERGZgifuTzDu5DhU21xN1cVKIJslYRZMKDMBO+vtRNOcTeN0IHvu3DkUKlRIBbLW1taYNm0aJk+erPWwyIRpmpndsGED+vbtiwULFqhAVgLVatWq4datWyEWfcusxkGDBmHp0qUoVaoUbt++jTZt2qi+etOnT9fkORAREYXH7fe3seTKEvz78F/46/3Vvh+cf1A9Yiukq6A6FcRlUlYgS9H269cPPj4+yJAhg1rZq0SJEloPjUycpsGsBKBymqFt27ZqW4LaXbt2qWBVgtavSU2NFIU3a9ZMbUtGt2nTpjh16lSsj50oOt/gvXwDPtiiwtMn6scwGZ9cgYdHAZ3xPWe9HvDzQ5zm6yErTiXWehgm48LrC6q9lrTZMiiVuhQ65OuAIimKxJmFDr5HFkDo2bOn+rlu3brqsz5xYv4dkQkHs/KtTE41DB48OHCfzFysXLkyTpw4EeJ9JBu7evVqVYpQrFgx3L9/H3///TdatmwZ6uN4e3urS9A6HSJjCmQbLDiBc4/eaz0U07KtK3BzJ4wxkN3ybgJe+uaC2Ygjy6bGxP/bR58dVUHs+dfn1T4LWKBKhioqE5s7aW6Ym+bNm6sAtmHDhujRo4fZBPEU8zR7F3JxcYG/vz9SpEgRbL9s37x5M8T7SEZW7lemTBn1RuHn54cuXbqolUFCM3HiRIwePTrax08UHSQjG92BbJEMieFgE8dXynF7HvCvcw7AwXgyO37+Nnj5ynwC2YSu92Cdp7rWwzC6HrGy1KyUE9x6f0vts7a0Rp0sddAmTxtkTJgR5kI+p6U8UFbzsrGxga2trWq7xZZbFN1M6iv1wYMHMWHCBMyfP1/V2N69exe9evXC2LFjMXz48BDvI5lfqcsNmpmViWNExubssMpwtI16ECqBrNlkPKqOBbJXg9Hw9gfOH1I/tp1SBjZ2cfNLhc7TE3dKl4GlzgcWiZtrPRyj4O3vjb/u/qV6xMoELyGTuBpmb4hWuVshhVPwxE1c9+7dOzWnZceOHWp5WkksCQayFKeCWWdnZ7XO8qtXr4Ltl+2UKVOGeB8JWKWkoEOHDmo7X758qtlyp06dMHTo0BD/J7Gzs1MXImMngayjrUl9v6QwSCAbZ4NZfytY6Xy0HoZR8PDxwKbbm7Dy+krVakskskuEZrmaoWmOpkhknwjmRua3NGnSBE+ePFHZ2PTp02s9JIrjNPvklD/wwoULY9++faoQXOh0OrXdvXv3EO/j6en5TcAqAbHhdAYREVFskMUNVl9fjfW31qtFD0QKxxRonac16merD0cbR5gb+QyXXrGSXJIywmzZsqn2WwUKFNB6aBTHaZoGktP/rVu3RpEiRdSELmnNJZlWQ3eDVq1aIU2aNIGnJ2rVqqU6IBQsWDCwzECytbLfENQSERHFlOcez1UpwdY7W/HJP6DHecYEGdEubzvUzFwTNlY2MEdv3rxRn+f//POP2pZOQ7IQQvz48bUeGpkBTYPZxo0bq/8BZCm7ly9fqm9vu3fvDpwU9vjx42CZ2GHDhqlaQPn32bNnSJYsmQpkx48fr+GzICKiuO7eh3tqgYO/7/8NP31A77U8SfOo9loV01WElaV5J1SkRvbw4cNqAaQ5c+agffv25lO7T5rTvEBPSgpCKyuQCV9ByUohI0eOVBciIqKYdvnNZdVe68CTA4H7iqcqjvZ526NEqhIM2D6TlTnXrFmDzJkzq/ksRGYVzBIRERkTmYNx4vkJLLm6BKdffllivVL6SiqIzZeMwZpM1payAmmNWa5cObWvTp06Wg+LzBSDWSIiIgD+On/se7xPBbHX315X+6wtrFEjcw1VE5s5UWath2gUZKK2LIAgAa0sXnTjxg3OWyFNMZglIiKz5uvvix33d2DZ1WV46PZQ7bO3skf97PXROndrpIqXSushGgXpUDBmzBjV212y13ny5FHdChjIktYYzBIRkVny9PUM7BH72vO12hffNj6a5WyG5rmaI7G98awup7Xnz5+rbKxhLotM8Jo9ezYcHc2vBRkZHwazRERkVj58+oC1N9eqi6u3q9qXzCGZ6hHbIHsDONk4aT1EoyKLH0hfeOk+5OTkpFpuSWBLZCwYzBIRkVl4+fElVlxbgT/v/AkvPy+1L3389Gibty1qZ6kNWytbrYdolNKmTYuKFSvi1q1bqqwge/bsWg+JKBgGs0REFKc9cH2g6mGlLtZPF9AjNmeSnGifrz2qpK9i9j1iQ/L06VPEixcPiRIlUu3HFi9erNpjOjg4aD00om8wmCUiojjp2ttrWHJlCfY+2gs9ApY8L5KiiApiS6cuzR6xodi1a5dqu1WhQgVs2rRJvU5cyYuMGYNZogiSWbxevv7RcixPn+g5jtHT64GLa4F396LneG7PEN2/Uz8fXZSP4+ttJr9PEwhiZ56biZMvTgbuq5C2ggpiCyQvoOnYjJmvr6/qGztt2jS1/eDBA7i6uqrsLJExYzBLFMGgp8GCEzj36L3WQzEtr28Af3WN/uPaOkXL73TL1PN4eT9gIhCZLvldrrq+CjPOz1DlBFYWVqieqbqqic2emHWeYXn06BGaNGmCkycDvgD06NEDU6dOhZ2dndZDI/ouBrNEESAZ2ZgIZItkSAwHmzhct+ftHvCvXQKgQDTNgk6YBkhfMsqHkYxsdAeyqbIkhLWtZbQek77foWDYsWE49PSQ2q6cvjJ+LfIr0sZPq/XQjN62bdvQtm1bfPjwAQkTJsTSpUtRr149rYdFFG4MZoki6eywynC0jZ4AVAJZs6jfc0wKVJ8EY9V2ShnY2EX9dyqBrFn8Po3E+VfnMeDwALzyfAVbS1v0L9ofjXM05u8gHLy8vNCzZ08VyBYrVgzr169HpkyZtB4WUYQwmCWKJAlkHW35v1BcIoFsdASzFHvLz8rSs/Mvzoe/3h8ZE2TE1PJTVacCCh/pTrBu3Tps3boVEyZMgK0t25OR6eEnMRERmRwXLxcMPjI4cJJXzcw1MazEMC54EA6bN2+Gt7d34MIHpUuXVhciU8VgloiITMqJ5ydUIPv201s4WDtgSPEhqJOlDssKvuPTp0/49ddfMX/+fJWRLVq0KBdAoDiBwSwREZkE6VAgJQWLryxWfWOzJsqKaeWnIUuiLFoPzejduXMHjRs3xoULF9S21MmyNpbiCgazRERkEkvRDjw8EOdfn1fbDbI3wMCiA2Fvba/10IyeTOrq2LEjPDw84OzsjJUrV6J69epaD4so2jCYJSIio3bwyUHVdsvV21XVxI4sOVL1j6Xv993t2rUrFixYoLbLli2rJnulSZNG66ERRSsGs0REZJR8/X3VAgiyEILInTQ3ppabivQJ0ms9NJMgNcSSiZV/hw4dipEjR8Lamh/7FPfwr5qIiIzOE/cnGHBoAK6+vaq2W+RqgT6F+8DWiq2jvkfKCeLFi6d+lgD2559/RsmSUV9ghMhYcYkaIiIyKv8+/BeNdjRSgWwC2wSYVXEWBhYbyED2Oz5+/Ih27dqhQoUKqvWWkEwsA1mK65iZJSIio/DJ7xOmnpmKjbc3qu0CyQpgSrkpSBUvldZDM3rXrl1Do0aNcP36dVhaWuLgwYOoVq2a1sMiihUMZomISHP3Xe+j/6H+uP3+ttrukK8DuhboChtLG62HZvSTvJYtW4bu3burpWlTpUqFtWvXquwskblgMEtERJrafm87xp0cBy8/LySxT4IJZSagdBquSPU97u7u+OWXX7BmzRq1XbVqVaxatQrJkyfXemhEsYrBLBHFCr1emt7bAt7+MCa+RjYec+Lp64nxp8arYFYUT1kcE8tORDLHZFoPzSR07txZtdqysrLC2LFjMXDgQFViQGRuGMwSUaycCt3ybgJevsoF9Dqk9XDICNx6dwv9D/fHA9cHsLSwxC/5f0HHfB1hZWml9dBMxrhx43D58mXVR7ZMmTJaD4dIMwxmiSjG+fkCL31zwZilypIQ1rbMasXGF5tNtzdh8unJ8NH5ILlDckwqNwlFUxbVemhGz83NDbt371YTvUTmzJlVMMtsLJk7BrNEFKvaTikDGzvjy75JICvN5SnmuPu4Y/SJ0ar1liiTpgzGlxmv6mQpbOfPn1dB7L1795AwYcLATgUMZIkYzBJRLJNA1hiDWYpZV12uqm4FTz2ewtrCGr0K9UKrPK1UiQGFncmeN28efv31V/j4+CB9+vQqmCWiLxjMEhFRjAZjshytLEvrp/NDaqfUmFp+Kn5I9oPWQzN6Hz58QPv27bFlyxa1Xbt2bdWGK0kSZrKJgmIwS0REMeLDpw8Yfmw4Dj49qLYrp6+MUaVGIaEdM4vfc+bMGTRu3BgPHjyAjY0Npk6dip49e7IUhigEDGaJiCjanX91HgMOD8Arz1dq4YMBRQegcY7GDMbC6caNGyqQzZQpEzZs2ICiRTlBjig0DGaJiCja6PQ6LL26FHMvzIW/3h8ZEmTA1HJTkSupcXezMJaSDEOw36pVK3z8+BFNmzZFokSJtB4akVFj5T0REUULFy8XdNnTBbPOz1KBbI3MNbCh5gYGsuFw/PhxlC5dGi4uLoH7ZHUvBrJE38dgloiIouzki5NosL0BTrw4AXsre4wpNQYTy0yEk42T1kMzajqdDlOmTEG5cuVw4sQJDBs2TOshEZkclhkQEVGkSYeCPy7MwaLLi6CHHlkTZcW08tOQJVEWrYdm9N68eYPWrVvjn3/+UdtNmjRRgS0RRQyDWSIiirSue7vilOsl9XP9bPUxsNhAOFg7aD0so3f48GFVD/v8+XPY29tj9uzZ6NChAyfIEUUCg1kiIoq0i28uwskpHkaWHInqmaprPRyTsG3bNtSvX1+VGOTIkQMbN27EDz+w7y5RZDGYJSKicPP198WcczNhCFtzJMmJiVWmI32C9BqPzHRUrFgRGTNmVBO+5s+fj3jx4mk9JCKTxmCWiIjC5an7U7Uk7Z0XVwKD2SVVF8M+Pmfcf8/ly5eRL18+VUYgy9GePn1areTFsgKiqGM3AyIi+q7/Hv6HRjsa4erbq4hnGz9wv62VrabjMnb+/v4YNWoUChQogN9//z1wf9KkSRnIEkUTZmaJKNQG7n4+umg5lq9vtByGNODt742pZ6Ziw60Najt/svyYXHQ03CbW1HpoRu/Fixdo3rw5Dhw4oLavXr2q9ZCI4iQGs0QUYiC7Zep5vLzvqvVQSEMPXB+osoJb72+p7fZ526NbwW6w+uQLN60HZ+T27NmDFi1a4PXr13BycsKCBQvUNhFFPwazRPQNycjGRCCbKsFLWNuyuskU7Li3A2NPjoWXnxeS2CfB+DLjUSZNGXWdDky1h8bPz0+VFUyYMEF9KZQuBRs2bEDOnDm1HhpRnMVglojC1HZKGdjYWUXuzudXAbsHA3p/IH1JWDddyjpBI+fp64kJpybgr3t/qe1iKYthYtmJSO6YXOuhmcxEr0mTJqlAtnPnzpgxYwYcHNh3lygmMZglojBJIBvhYFanA/aNBo7NBCR2zd8EqD0bsLaLqWFSNLj9/jb6HeqnygssLSzRJX8XdMrXCVaWkfwyY4YKFSqEqVOnInXq1GjcuLHWwyEyCwxmiSh6+X4CtnUBrm0N2C4/CKgwCGBG1mhJFnHznc2YfHqymvCVzCEZJpebjKIpi2o9NKPn6+uLkSNHomXLlsiVK5fa16dPH62HRWRWGMwSUfT56AKsbwY8OQVY2gRkYws003pUFAYPHw+MPjEaux/uVttSFyv1sVInS2F7/PgxmjRpghMnTmDHjh04f/48bGxstB4WkdlhMEtE0cPlLrCmAfD+AWCXEGiyGshUTutRURiuuVxTZQVPPZ7C2sIaPQv1ROs8rVWJAYVt+/btaNOmDd6/f68WQZBJXwxkibTBYJaIou7R8YCMrNd7IFF6oPlmIFkOrUdFYZQVrLmxBr+d+w1+Oj+kdkqNKeWnqB6yFDYfHx8MHDgQM2fOVNtFixZV3QoyZcqk9dCIzBaDWSKKmsubgL+6Av4+QJrCQNP1QDzOfDdWrt6uGHZsGA4+Oai2K6WvhNGlRiOhZNMpTG/evEGNGjVw5syZwNpY6Vxga8tV0Ii0xGCWiCJHrweOTAP2jwvYzlkTqLcIsHXUemQUiguvL2DA4QF4+fElbCxt0K9IPzTN2ZTt0sIpceLEsLe3V/8uX74ctWvX1npIRMRglogixd8X2NkbuLA6YLtkd6DKGIAtnIySTq/D0qtLMffCXPjr/ZE+fnpMKz8NuZIGzL6n0Hl7e6tgX7Kv1tbWWLdunVoYIUOGDFoPjYg+YzBLRBHzyRXY2Aq4fxCQiULVpwDFOmo9KgqFi5cLhh4diuPPj6vtnzP9jBElR8DJxknroRm9u3fvolGjRihfvrxa/ECkSZNG62ER0VcYzBJR+H14DKxpBLy5AUgw1GApkOMnrUdFoTj14hQGHRmkAlp7K3sMKT4EdbPWZVlBOMikro4dO8Ld3R1PnjzB0KFD4ezsrPWwiCgEDGaJKHyenQfWNQE8XgHxUgLNNgCpC2g9KgqBdChYcGkBFl5eCD30yJooK6aWm4qsibNqPTSj5+Xlhd69e2PhwoVqu2zZsli7di0DWSIjxmCWiL7v5t/An+0BX08geR6g+UYgYVqtR0UhePXxFQYeGYhzr86p7frZ6mNgsYFwsHbQemhG7+bNm6qs4MqVKyp7PWTIENU/Vmplich48f9QIgrb6cXAvoHSvgDIUglouBywT6D1qCgEh58eVvWxH7w/wNHaUdXG1shcQ+thmcxEr8qVK+PZs2dInjw5Vq9ejSpVqmg9LCKK6WD206dPqk0JERlPM3w/H12Uj+Pr7f9lY89wwFIPFGoN1PgNsOIqR7H9O9V7eYV5G19/X/x+6XesvhHQXeKHJDnVkrTp46eDztMz2sek+854TJGdnZ2a5PX7779jzZo1SJUqldZDIqKYCmZ1Oh3Gjx+PBQsW4NWrV7h9+zYyZ86M4cOHI2PGjGjfvn1ED0lE0RT0bJl6Hi/vu0b/wSuPAkr3BjhxKNZ/p4+aNYfXhQvfvW21z5cA1+CFurgVw+MzddeuXcPbt29RrlzAsssNGzZEgwYNOEGOyMREeAHucePGqWbRU6ZMCbbqSd68ebF48eLoHh8RhZNkZKM7kE1lcwPW5XoAZfowkNWAZGTDE8hqxaFQIVg4OJjkl4Rly5appWgleH3x4kXgdQxkicwgM7ty5Uo1y7NSpUro0qVL4P78+fOr4nki0l7bKWVgYxfFBQy2dob1jU2wiD81uoZFUZDt2FFYfg4cvf19MPP8TPx5e7PazuucV5UVpHKK3VPjEsiaWvDn4eGBrl27YtWqVWq7TJkysLLiYh9EZhXMSnF81qxZQyw/8PX1ja5xEVEUSCAb5WDWyg8wrTglTpNA1tLREQ9dH6LfoX649f4WYGuBtnnbokfBHmp5Wgrb5cuXVbeCW7duwdLSEmPHjsWgQYPUz0RkRsFs7ty5ceTIkW+W8tu8eTMKFiwYnWMjIqIgdtzbgbEnx8LLzwuJ7RJjQtkJKJOmjNbDMomygkWLFqFXr15q4rKs4iXL0koPWSIyw2B2xIgRaN26tcrQSjZ2y5Yt6luulB/s3LkzZkZJRGTmxpwciz+fBLzHFk1ZFJPKTkJyx+RaD8skSCnEsWPHVCBbvXp19XnFRRCI4o4In1upU6cOduzYgb1798LJyUkFtzdu3FD72JOPiChm7Ly3AxawQNf8XbGoyiIGsuHMyBrMmzdPdeGRpAsDWaK4JVJ9ZuXUzJ49e6J/NEREFBiIbb27Dbk/bzs7OGNMpSkolqqYxiMzjddu/vz52L9/PzZt2qRqYuPFi4fOnTtrPTQiMobMrPSUlb58X/vw4YO6joiIosbDxwMDDw/ExFMTAvet/nk1A9lwkM8imeTVvXt3VQa3detWrYdERMaWmX348CH8/YOsDhRkKUCpoyUiosi79vYa+h/qjyfuT+BoIR0pAt5vk9gn0XpoRu/MmTNo3LgxHjx4ABsbG9UPvV69eloPi4iMJZjdvn174M///vsvEiZMGLgtwe2+ffvUCmBERBS5U+Nrb67FtLPT4KfzUz1jpxQdA0xrq/XQTOK1mzVrFgYMGKBaRMpn0caNG9WiCEQU94U7mK1bt27grFDpZhCUfAOWN4/ffvst+kdIRBTHuXq7Yvix4Tjw5IDa/jHdjxhTegzi+9twSdpw6NmzJ+bOnat+lkzskiVLkChRIq2HRUTGFsxKGy6RKVMmdSqHs0GJiKLu4uuLGHB4AF58fKEWPvi1yK9olrOZShzoPD21Hp5JaNWqlVpmfdKkSWp1L1NblYyIYrlmVmqRiIgoanR6HZZdXYY5F+bAX++P9PHTY2r5qcid1NC/gMJKrshqXgUKFFDbUk7w6NEjJEnCumIicxSpNfw+fvyIv//+W/Xsmz17drBLREnvPylRsLe3R/HixXH69OnvzlTt1q0bUqVKBTs7O2TPnl2NhYjIVLz1eouue7ti5vmZKpCtnqk6NtTcwEA2HFxcXFCrVi2UKFECFy9eDNzPQJbIfEU4M3vhwgX8/PPP8PT0VEGtvIHIm4ujoyOSJ0+uapfCa8OGDejbt68KiiWQnTlzJqpVq6ZWFJNjfc3Hx0ctzCDXyfK5siShfBtnbRQRmYrTL05j4JGBcPFygb2VPQYXH4z/Zf0fT42Hgyyl3rRpU9U5R5IZ8llhyM4SkfmKcDDbp08f9a1YAlDpaHDy5Ek1AaxFixZq3euImD59Ojp27Ii2bQNm68oxd+3ahaVLl2LQoEHf3F72v3v3DsePH1ePKdhBgUyavx/w8DDg7RH1Y/nKf+IF/HxzFxDwv0jkuT2P+pgokL/OHwsuL8Afl/6AHnpkSZhFlRVkS5xN66GZRFmB1MPKipPSPUfOyMliCD/88IPWQyMiUwxm5bTOH3/8oVZUsbKyUv1lZbEE6ecnXQ7C29NPsqznzp3D4MGDA/fJMStXrowTJ06E2h6sZMmSqszgr7/+QrJkydCsWTMMHDhQjSUkMj65GLi5uUX0KRPFnLNLgH8GRM+xdHYA1gf8/GcHwPLL332UWIb8/xaF36uPrzDoyCCcfXVWbdfLVg+Dig2Cg7WD1kMzeq9fv0bLli3x33//qW1JnPz+++9qRS8iokgFs5IRlaBTyOn+x48fI1euXCpL++TJk3AfR0oT5Bt2ihQpgu2X7Zs3b4Z4n/v376vlCZs3b67qZO/evatmrkpfwZEjR4Z4n4kTJ2L06NEReo5EscaQ/YyXEkgcxbMM/jbA688/py0GWKlUbdQ4JgVy/Bz145ixI0+PYOjRoXjv/R6O1o4YUXIEamSuofWwTMbq1atVIOvg4KDmWLRp04YlGUQUtWC2YMGCqjVXtmzZUL58eXXaRwLTVatWIW/evIjpU00SQC9cuFBlYgsXLqxqp6ZOnRpqMCuZX6nLDZqZTZcuXYyOkyjC8jUAqo2P2jG8/YHzhwJ+bv0XYMeMqpZ8db6Yc34Oll1bprZzJsmJaeWnIUOCDFoPzaT07t0b9+7dU4mLPHnyaD0cIooL3QwmTJigOgmI8ePHI3HixPjll1/w5s0bVX4QXtKnVgLSV69eBdsv2ylTpgzxPvK4UisVtKRAssIvX75UZQshkUkCCRIkCHYhIopJzzyeoc3uNoGBbNOcTbH659UMZMPhxYsX6jPFy8tLbcuZQMnIMpAlomjLzBYpUiTwZ8mS7t69G5Fha2urMquyDK5hdTHJvMp29+7dQ7xP6dKlsXbtWnU7Q6nD7du3VZArxyMi0treR3sx4vgIuPu4I75tfIwpNQaVM1TWelgmYc+ePaomVupkra2tMWfOHK2HRERxtc9sSM6fP4+aNWtG6D5y+n/RokVYsWIFbty4ob6NS7svQ3cDWdUl6AQxuV66GUjXBAlipfOBZIplQhgRkZa8/b0x4dQE9DnYRwWyPzj/gE21NjGQDQc/Pz8MGzZMtWaUQDZfvnx8XyeimMnM/vvvv+qbs2RBO3TooLoYyGQtaaO1Y8cO9UYUEY0bN1blCVJ3K6UC0i9QMr2GSWEyucyQgRVS6ypjkPZg0pJF+sxKYCvdDIiItPLQ9SH6H+6Pm+8CJq+2zdMWPQr1UMvTUthk3oP0jpUesqJTp06q57hM+CIiitZgdsmSJaonrCyS8P79eyxevFj1ie3Ro4cKSq9evarqVyNKSgpCKys4ePDgN/ukNZf0tiUiMga77u/CmBNj4OnnicR2iTG+zHiUTVtW62GZhGPHjqkyM5lELK225ExdkyZNtB4WEcXVYHbWrFmYPHky+vfvjz///BMNGzbE/PnzceXKFaRNmzZmR0lEZGQ8fT0x6fQkbL27VW0XTlEYk8tORgqn4O0GKXTp06dXcyCkS46sCCldcoiIYiyYldYoEsAKWRhBivOlJRYDWSIyN3ff30W/Q/1wz/UeLGCBzvk7o/MPnWFtGeE5tWbH1dVV9SU3lI5J7/AcOXLA3t5e66ERUVyfACZtUhwdHdXP0rBaWl4ZWnQREZkDvV6PP2//iaa7mqpA1tnBGYuqLkK3At0YyIaDzK2QuRaymqNB/vz5GcgSUZRE6N1X6mQNSwjK7NPly5erfrFB9ezZM2ojIiIyQh4+Hhhzcgz+efCP2i6VuhQmlJmApA5JtR6a0ZM+4NKZRuZZCClRq127ttbDIiJzC2altkmK8w1kYQNZ9SsoydgymCWiuOb62+vof6g/Hrs/hpWFFXoU7IG2edvC0iLauhvGWQ8ePFCTuk6fPh24opfMvyAiivVg9uHDh9H2oEREplJWsPbmWvx29je1PG0qp1SYUm4KCiQvoPXQTMKWLVvQrl07VSebKFEidTavTp06Wg+LiOIYFnkREYXA1dsVI46NwP4n+9V2xXQVMbb0WCS0C5i8RGG7cOEC6tevr34uUaIE1q9fjwwZuJwvEUU/BrNERF+5+PoiBhwegBcfX6iFD34t8iua5WymSqkofKTdlqzaKPMsxo8fDxsbLiBBRDGDwSwR0Wc6vQ7Lry3H7POz4a/3R7r46TC1/FTkSZpH66GZhM2bN6NMmTJqToWYN28evwAQUYzj7AUiIgBvvd6i696umHFuhgpkq2esjo01NzKQDWfrxi5duqhe5M2bN4e/v7/az0CWiGIDM7NEZPbOvDyDgYcH4o3XG9hZ2WFwscGol60eg7FwuHXrFho1aoTLly+r10vqY2XiHBGRUQezshrYsmXL1L+yzG3y5Mnxzz//qPZdefIwi0FEpsFf54+FlxdiweUFqsQgc8LMmFZ+GrIl5rKq4bFmzRp07twZHz9+RLJkybB69WpUrVpV62ERkZmJcJnBoUOHkC9fPpw6dUq1XfHw8FD7L126hJEjR8bEGImIot1rz9fouKcj5l+arwLZ/2X9H9bVWMdANhw8PT3RoUMHtGjRQgWyFSpUwMWLFxnIEpFpBLODBg3CuHHjsGfPHtja2gbu//HHH3Hy5MnoHh8RUbQ7+uwoGmxvoMoLHKwd1EpeY0qPgaNNwJLdFDadTodjx46psgJJYuzduxepU6fWelhEZKYiXGZw5coVrF279pv9Umrg4uISXeMiIop2svDBnAtzsOzqMrWdM0lOTC03FRkTZtR6aCZBamElgJV2Wxs3bsTr169RqVIlrYdFRGYuwsGsrOLy4sULZMqU6ZsG2WnSpInOsRERRZvnHs/R/3B/XH5zWW03ydEE/Yr2UxO+KGxSTtatWzfkz58fffv2Vfuk3IyIyCSDWVlje+DAgdi0aZP6hm443dSvXz+0atUqZkZJRBQF+x7vw/Bjw+Hu4474NvExuvRoVMlQRethmQQ5GyfdCm7evAl7e3vVeitFihRaD4uIKPI1sxMmTEDOnDmRLl069W09d+7cKFeuHEqVKoVhw4ZF9HBERDHGx98HE09NRO8DvVUgm885HzbW2shANpwlBYsWLUKxYsVUICs1sf/++y8DWSIy/cysTPqSN7jhw4fj6tWrKqCVZQuzZeMMYCIyHo/cHqH/of648e6G2m6Tpw16FuwJGysuq/o9bm5uquXW+vXr1fZPP/2ElStXqvZbREQmH8wePXpULVcoPWXlQkRkbHbd34UxJ8bA088TiewSYXyZ8SiXtpzWwzIJvr6+KFmyJK5fvw4rKyt1Nk7KyCwtuWAkERmnCL87SQsumfw1ZMgQ9WZHRGQsvPy8MPL4SAw6MkgFsoVTFMamWpsYyEaAjY0N2rdvr0rJDh8+jAEDBjCQJSKjFuF3qOfPn+PXX39ViyfkzZsXBQoUwNSpU/H06dOYGSERUTjcfX8XzXY1w5Y7W2ABC3TJ3wWLqy5GSqeUWg/N6Lm6uuLOnTuB23369FETv2QuBBFRnAtmnZ2d0b17d9XBQJazbdiwIVasWIGMGTOqrC0RUWxPVNp6Zyua7mqKux/uwtnBGQurLkS3At1gbRmpFbvNytmzZ9W8h5o1a8Ld3V3tk041CRMm1HpoREThEqVzR1JuICuCTZo0SfUclGwtEVFs+ej7UZUUjDg+Ap/8P6FU6lKqrKBEqhJaD80kvgTMmjVLZV8fPHgAHx8fPHv2TOthERFFWKTTFpKZXbNmDTZv3oxPnz6hTp06mDhxYmQPR0QUITfe3kC/Q/3w2P0xrCys0L1gd7TL2w6WFpaRCuz0Xl4wNroYGtP79+/Rrl07bNu2TW3/73//w9KlS9WiOEREcT6YHTx4sGrXIrWzVapUUd/sJZB1dOSa5mQemlvtRVXLs7Bbv1jOx0btYG+/1ClS+APPdTfXYdrZaWp5WqmJnVJuCgomLxjp4z1q1hxeFy7AHJw8eVItfvPo0SPVavG3335Tq3tJaQERkVkEszK7tX///mpFGKmfJTI3g63XIp7FJ+B+NB40XvJoPFjc5ertqroVyIpeokK6ChhXehwS2kW+vlMyssYeyDoUKgQLB4doOdaYMWNUIJslSxZs2LABhQsXjpbjEhGZTDAr5QVE5swG/upfn8rjYBs/GprI2zoBWbki1fdcenMJAw4NwPOPz9XErl8L/4rmuZpHa0Yx27GjsIymoDE6SSAbXc9TyglGjx6NyZMnI0GCBNFyTCIiow9mt2/fjurVq6v+g/JzWGrXrh1dYyMyav45agHJMmo9jDhPp9dhxbUVmH1+Nvz0fkgbLy2mlZ+GPM55ov2xJJC1jGMlU7LQzX///acysiJlypT4/ffftR4WEVHsBrN169bFy5cvkTx5cvVzaCRz4O8fkLUiIoqqd5/eYejRoTj67Kja/injTxhRcgTi28bXemhGT6fTqeyrLD0u78uFChUK8/2biChOB7PyphjSz0REMeXMyzMYdHgQXnu9hp2VHQYVG4T62epzolI4vH79Gi1btlQZWdGiRQtUrlxZ62EREcWICPewWblyJby9vb/ZLz0K5Toioqjw1/nj94u/o8N/HVQgmylhJqytsRYNsjdgIBsOBw8eVCszSiDr4OCAJUuWqPfmePHiaT00IiLjCGbbtm2rlj78mqwcI9cREUXWa8/X6LSnE+Zfmq9qZetmrYv1NdYje+LsWg/NJMyYMQOVKlXCixcvkCtXLpw5c0b1k+WXACKKy6wj05MxpDfGp0+fcvlDIoq0Y8+OYcjRIapO1sHaAcNLDEetLLW0HpZJyZo1qyoFa9OmDebOnQsnJyeth0REZDzBrKzdLUGsXOSbv7X1l7vK5AJZDvGnn36KqXESURwlCx/MvTAXS68uVds5EufA1PJTVXkBfd+HDx8CV+6qVauWysYWKVJE62ERERlfMGuYBXvx4kVUq1YtWP2VrCKTMWNG1K9fP2ZGSURx0nOP5xhweIDqISsa52iM/kX7qwlfFDY/Pz/VL3bBggU4d+4c0qdPr/YzkCUicxPuYHbkyJHqXwlaGzduDHt7+5gcFxHFcfsf78fwY8Ph5uOGeDbxMLrUaFTNWFXrYZmEZ8+eoVmzZmpFRrF582b07dtX62EREZlGzWzr1q1jZiREZBZ8/H0w/dx0rLmxRm3nc86HKeWmIG38tFoPzSTs3r1btd1ycXFRZ8gWLVqEJk2aaD0sIiLjDmaTJEmC27dvw9nZGYkTJw5zZuy7d++ic3xEFIc8dnuMfof64ca7G2q7de7W6FWoF2ysbLQemtHz9fXFiBEjMGnSJLUt7bc2btyIbNmyaT00IiLjD2al3Uv8+PEDf2abFyKKqH8e/IPRJ0bjo+9HJLJLhPFlxqNc2nJaD8tkzJo1KzCQ7datG6ZNm8ZyLyKi8AazQUsLpOULEVF4efl5YfLpyfjzzp9qu1DyQphcbjJSOqXUemgmRQLY7du3o2fPnmjQoIHWwyEiMt2a2fPnz8PGxgb58uVT23/99ReWLVuG3LlzY9SoUaqzARGZPukprffyitIx7rs+UL1j73+4B3tYoE3etuiYrwOsLayh8/SEMdBF8TnGFFlVcenSpejYsSOsrKzUal6HDh3imTEioqgGs507d8agQYNUMHv//n3V2aBevXrYtGkTPD09MXPmzIgekihmvb0HrGsKeLpE+VAOesDCwhfmEMg+atYcXhcuRPlYo4NtLcI9LIryMeO6hw8fqvfW06dP482bNxg+fLjaz0CWiCgaglmZCCYTD4QEsOXLl8fatWtx7NgxNaOWwSwZnQeHAJdb0XIoQyjxRp8ATk7OiKskIxsdgawpcShUCBYODloPA1u3blVL0BoWQ/jhhx+0HhIRUdxbzlaWSxR79+5FzZo11c/p0qVTrWKIjFam8kD1KVE6hJevH2rNPYbnemectTaPyTfZjh2FZTiDvFvvb6uygiduj2FlYYXO+bugVe6WsLSwhLGTQFbLzKe3tzf69++POXPmqO0SJUpg/fr1yJAhg2ZjIiKKk8GsrC4zbtw4VK5cWdVv/f7772q/LGebIkWKmBgjUfSwiw8kzxmlQ+h9/HBX/wjmRAJZS0fH737JXX9rPaaemaqWp02RKKVakrZg8oKxNk5Tdu/ePVVWICt5iX79+mHChAlqfgIREUVzMCtlBM2bN8e2bdswdOhQZM2aNXAFmlKlSkX0cERk4mQFr5HHRmLv471qu0LaChhbeiwS2SfSemgmw8PDA1evXlU9vVeuXIkaNWpoPSQiorgbzEr91pUrV77ZP3XqVDXjlojMx+U3lzHg8AA883gGa0tr9C3cFy1yteBEpXCQbLbhdcqfPz82bNiAQoUKqZItIiKKwWDWQE6H3bgRsIqPtOWSN2EiMg86vQ4rr63ErPOz4Kf3Q9p4aTGt/DTkcc6j9dBMgkykbdGiBebOnYtixYqpfXXq1NF6WERE5hHMvn79WtV2Sb2szLQVMuu2YsWKarJCsmTJYmKcRGQk3n96j6FHh+LIsyNqu1rGahhZciTi2wasEkhhk+4v0uJQSgt69OiBkydPMpNNRBQFEZ5iLG++8iZ87do1vHv3Tl2k1svNzU2tTENEcdeZl2fQYHsDFcjaWdlheInhmFpuKgPZcJA+3B06dFBzDuQ9tEKFCmruAQNZIqJYzszu3r1bteTKlStX4D4pM5g3bx6qVq0axeEQkTHy1/lj0ZVF+P3S76rEIFPCTCqIzZEkh9ZDMwlSktWoUSP1xV+C1xEjRqiFEDjPgIhIg2BWesyG1C5G9hn6zxJR3OHi5YIhR8bg1MtTart2ltoYWnwoHG3CbtdFAeQsltTFSmZW2hdKmcGPP/6o9bCIiMw3mJU34V69emHdunVInTq12vfs2TP06dMHlSpViokxEpGGmv/dAi917+Fg7YBhJYapYJbCT85cyfuml5cXVq9ejZQpU2o9JCIi8w5mZfZt7dq1kTFjxsAWMk+ePEHevHnVGzURmT5Z+MDg/ad3yJ4ih1oEIXPCzJqOy5SysbJyV7x48VRZgXz5d3BwYFkBEZExBLMSwJ4/fx779u0LbM0l9bOyIhgRmb4XHi8wdM+v6P95u372BuhTehDszWT53qj2jl2yZImaKNugQQO1AIIEsxLUEhGREQSz0tR7+/bt8PHxUSUF8oZNRHHHgccHMOzYMHh7uAbuG1h0ACwZyH6Xu7s7unTpompihYuLC7y9vWFvz9eOiMgogtnff/8d3bp1Q7Zs2dTpsi1btqj1xGXlLyIybT7+PphxbgZW3wgoFSqYJDeAb1f6o5BdvHhRdSu4c+eOKiWYMGEC+vXrB0vLCHc/JCKiCLKMSK3syJEjcevWLfXGvWLFCsyfPz+ij0ekGT+dHp4+flG8+COueeL2BC3/aRkYyLbK3QqLqi7SelgmU1YgX/RLlCihAlkpwzp8+DAGDBjAQJaIyNgys/fv30fr1q0Dt5s1a4b27dvjxYsXSJUqVUyNjyhq9HroX16DtKXfe+M1uoz4V+sRGZXdD3Zj1IlR+Oj7EQntEmJ86fEon648dJ6eWg/NJLx//x6jRo1S5QS1atXCsmXLkDRpUq2HRURkVsIdzMqbtZOTU+C2ZB1sbW1Vuxkio+TvC+zsA4sLq9Tmfl2BaDt0kQyJ4WBjujPTP/l9wuQzk7H59ma1XSh5IUwuNxkpndg2KiKSJEmCNWvW4MqVK+jduzdX8yIiMvYJYLJijaPjl0bpMhFs/PjxSJgwYeC+6dOnR+8IiSLjkyuwsTVw/wD0FpYY4dMKG/0r4uywynC0jXoQKoGsqQYu9z/cx6+HfsXdD3dhAQt0/KEjfsn/C6wtI9zcxCzLCubMmaN6bEu3AiGdXNjNhYhIO+H+9CpXrpyqlw2qVKlSqvzAwFQ/3CmO+fAEWNsIeH0dsHGEd93FWBWQnFWBrKOttdkGYn/d+wsTTk2Al58XktonxcSyE1EydUmth2YyJQXt2rXDtm3bED9+fJQsWRJp0qTRelhERGYv3J/qBw8ejNmREEWH5xeAtY0Bj1dAvJRAsw3QOecFYN61slITO+7kOOy8v1Ntl0hVQgWyzg7OWg/NJJw6dQqNGzfGo0ePVHmVdCswrIBIRETaMs8UFcVNt/4BNrcDfD2B5HmA5huBhGkBHz+Ys5vvbqL/of546PYQlhaW6F6gO9rna69+prDpdDrMmDEDgwYNgp+fH7JkyaL6bRcuXFjroRER0WcMZiluOPUHsHsQoNcBWX4EGq4A7BPAnElZwYZbGzD1zFT46HyQwjEFppSbgkIpCmk9NJMgwWu9evWwY8cOtS19ZBctWoQECcz774qIyNgwmCXTpvMH/hsGnPzc87hQK6DGdMDKBubMzccN40+MxZ5He9R2+bTlMa70OCSyT6T10EyGtbU1smbNCjs7O8yaNQudOnXivAAiIiPEYJZMl89H4M+OwK1dAduVRgJl+shMRJi7Vv+0wmOvh6pDQZ9CfdAyd0sGYuEsK3Bzc0OiRAFB/6RJk9ChQwfkzi0rohERkTFiMEumyf0VsK5xwIQvKzvgf78DeevDnOmkxOKz5x7PkSZhGkwrPw151QQ4+p43b96gVatWcHd3x4EDB2BjY6MmezGQJSIybpGaAXLkyBG0aNFCtaZ59uyZ2rdq1SocPXo0usdH9K3XN4DFlQMCWYckQOvtZh/Ivv/0XvWONaiUvhI21drEQDacDh06hAIFCmD37t04f/48Lly4oPWQiIgopoLZP//8E9WqVYODg4N6w5eVwYSrq6tqV0MUo+4fBJZUA1wfA0myAB32AulLwJydfXkWDXY0wLFnxwL3TSgzAfFt42s6LlPg7++PsWPH4scff8Tz58+RK1cunD59GsWKFdN6aEREFFPB7Lhx47BgwQI1q1dOwxmULl1aZTSIYsyFNcDq+oC3K5C+ZEAgmzQLzJW/zh9/XPoD7f9rj9eer5EhfvrA61gf+30vX75UX8xHjBihamXbtGmDM2fOIG9eZrOJiOJ0MCurgMlqYF+TJW0/fPgQXeMi+kKvB/aPA/7qCuj8AkoKWm4DHJPAXLl4uaDz3s6Ye3GuqpWtnaU2VlRfqfWwTIrUx+7bt08t0b1ixQosW7YMTk5OWg+LiIhiOphNmTIl7t69+81+qZfNnDkzImPevHnImDEj7O3tUbx4cXWaLzzWr1+vMlB169aN1OOSCfDzBrZ0BA5PDdgu+ytQbzFgYw9zdfz5cdTfXh+nXpyCg7WDark1vsx4ONo4aj00kzJ79mxV93/u3DkV2BIRkZkEsx07dkSvXr3U8o4SSEqd2Zo1a9CvXz/88ssvER6ArKbTt29fjBw5UpUp5M+fX536e/36dZj3e/jwoXrMsmXLRvgxyUR4vgNW1gWubAIsrIDac4BKIwBL81y5yk/nh9nnZ6PLni549+kdsiXOhvU116NO1jpaD80kyHvV2rVrA7dz5syJY8eOqX+JiMiMWnPJso5SX1apUiV4enqqkgNpKi6BZY8ePSI8gOnTp6sAuW3btmpb6nF37dqFpUuXqscKbdJG8+bNMXr0aNVZgeUNcdC7+8CahsDbu4BdAqDRioCVveLoSl16L68wb/PK8xWGHR2GS28uwRZAvWz10btQb9hb20Hn6aluo/P2D7y97NP5W0V6TLrvjMfU/Pvvv6oDy7t375A2bdrAUinWFhMRmWEwK2/+Q4cORf/+/VW5gYeHh+rDGC9evAg/uI+PjzrFN3jw4MB9lpaWqFy5Mk6cOBHq/caMGYPkyZOjffv2KpgNi3RbMHRcENIQnUzArl8DAtkEaYHmm4AUueNsIPuoWXN4haMVVL9gWxvxCBuD7fG3tAXKzVA/3yldBlY6H5g7WZJ2+PDhavEDIe23pFSKiIjijkgvmhAdzcRdXFxUljVFihTB9sv2zZs3Q7yP1OYuWbIEFy9eDNdjTJw4UWVwycS4Pg34t86cOBvICsnIhieQ1YpDoUKwcHCAKXry5AmaNm2qSglE165d8dtvv6nafCIiMuNgtmLFimGemtu/fz9iiqzM07JlS9UWzNnZOVz3kayv1OQGzcymS5cuxsZI0cxKTqqbh2zHjsLyc+D41P0phh4dihvvbqjtpjmbonuB7rCx+tIO72u+3v44NPhM4LFs7CJfZmAggawpnoqXUiWZ1CVlBQkSJMDixYvRsGFDrYdFRETGEMzKabqgfH19VZb06tWraN26dYSOJQGplZUVXr16FWy/bId0KvDevXtq4letWrUC90n9rrC2tlZtw7JkCd53VOp55UJk7CSQtXR0xO6HuzH6+Gh4+HogYfxEqltBhXQVvn9/qy81s3Icy2gIZk3V48ePVSBbuHBhNcn06/cFIiIy42B2xoyAmryvjRo1StXPRoSUKsiHjfR6NLTXkuBUtrt37/7N7WXW8ZUrV4LtGzZsmMrYzpo1ixlXMmmf/Lwx7cQ0bLq9SW0XTF4QU8pNQUon1niGt/7YkEXu0qWLWqVQygz4ZZaIKG6LdM3s12SmsCwBOW3atAjdT0oAJKNbpEgRdf+ZM2fi48ePgd0N5FRhmjRpVO2r1Lp9vTpPokSJ1L9ctYdMXdv/2uL6x3uwgAU65OuArgW6wtoy2v4XjdO2bdumlqWVL8LyniBBrazoRUREcV+0fVJK94HITKxo3Lgx3rx5o5aUlOUlpYxh9+7dgZPC5HShdDggiuvuvb+LJAmSYmLZiSiVupTWwzEJ0qlk4MCB6syMkAleEtQSEZH5iHAwW69evW9O7b148QJnz55VLXAiQ0oKQiorEAcPHgzzvsuXL4/UYxJpzdPXE5NOjEbTz9tFUhbFuMpT4ewQvsmN5k5q6OXLsLT3E9LrWr4UExGReYlwMJswYcJg25I1zZEjh+r9WrVq1egcG1GcdevdLfQ71A8vXB4EBrNzKs6GjUN8jUdmGjZt2oQOHTqo7iRJkybFihUrUKNGDa2HRURExh7MSk9YqWXNly8fEidOHHOjIoqj5EyGTPCafHoyfHQ+SOuYDMBLdZ2Vpfl2H4iIhQsXonPnzurn0qVLY/369WpVLyIiMk8RKkaVNlqSfeXysUQR5+bjhl8P/YqxJ8eqQLZc2nJY/fMarYdlcqTUSTqXSA9pKUNiIEtEZN4iXGYgXQPu37+PTJkyxcyIiOKgqy5XVVnBM49nsLawRu/CvdEqdyu1AthrrQdnAmSCacmSJQP7U1+7dg3x47Mkg4iIIpiZFePGjVMTLXbu3KkmfknNWtALEQUvK1hxbQVa/tNSBbJp4qXByuor0TpPa5NcWSu2eXl5oWPHjihVqlSwyZ4MZImIKMKZWZng9euvv+Lnn39W27Vr1w72YWxoWC51tUQEfPj0AcOODcOhp4fUdpUMVTCq1CgksE2g9dBMwo0bN9CoUSO1uqC8t8iXZyIiokgHs6NHj1ar6hw4cCC8dyEyW+dencPAwwPxyvMVbC1tMaDoADTK0YjZ2HBauXIlfvnlF3h6eqqe02vWrEGlSpW0HhYREZlyMCuZV1G+fPmYHA+RSfPX+WPJ1SWYd3EedHodMibIiGnlpyFHkhxaD80kyOp/0nPaUFJQuXJlrF69OnARFSIioihNAGNWiUyWHrAB4OvtD1991P6O5YudTNz62jvvdxh7YhzOvzoHB1ihasaf0adwHzhaOMD7vXuIx9J98oK/pa36WcZmaRX5Mh25v6mTxVekZ6z0r5azQdKxQLqoEBERhcZCb0i5fod8uMiCCd8LaN+9ewdjJpPU5Hm4uroiQQLWLhqtuUUBl9tAm11AxjJROtRHb1+M73sQafzNJyjqNKs8bOxM8/lOnz4dhQsX5lkgIiIz5haBeC1CmVnJlHy9AhiRsfPz0ZlVIJsqS0JY20a4UYkm3N3dVXeUAQMGIEuWLGpf3759tR4WERGZkAgFs02aNEHy5MljbjREMazJ+BJIEM8u0vfXeXriTumATHGCHWsx7sJUXHa5rLZrZa2N7gW6w94q4se3cHCItjIeCWRNoSTo0qVLqlvB7du3cfnyZRw/ftwkxk1ERCYazPJDhuICa1urKJ1+1/lbwUrno35uc6wT3ujd4JTACaNKjsJPmX6KxpHGXVLZ9Mcff6B3797w9vZWK3hNmzaN7zFERBQ73QyIzJmvv2/gz27ersidKg+mlZuGdAnSaTouUyG1T506dcLGjRvVds2aNVXngqRJk2o9NCIiMlHhLqzT6XQsMSCz9sT9CTr+1zFwu0nOJlhVfRUD2XB68OCBmtglgay1tTV+++03bN++nYEsERHFXs0skbn69+G/GHV8FHw/fmmx1bdwX1haBbTVou9LkyYNEidOjAwZMmDDhg0oXry41kMiIqI4gMEsURg++X3C1DNTsfF2wGnxos4/ALig9bBMxocPHxAvXjyVibW1tcWWLVvUtgS1RERE0cE0+vcQaeC+6300/7u5CmQtYIEO+Trg9yq/az0sk3H69GkULFgQI0eODNyXLl06BrJERBStGMwShWD7ve1osrMJbr+/jST2SbCg8gL0KtQLNpayjhh9b7KoLHxQunRpPHz4UNXIyjK1REREMYFlBkRBePp6Yvyp8SqYFcVTFsfEshORzDGZ1kMzCbICYJs2bbBjxw613bBhQyxatAhOTk5aD42IiOIoBrNEn916dwv9D/fHA9cHsLSwRNf8XVVpgZWl+aweFhWy6IEsrPLkyRPY2dlh5syZ6Ny5M/vHEhFRjGIwS2ZPTotvur0Jk09Pho/OB8kdkmNyuckokrKI1kMzqf6xP//8s/o3W7ZsqrSgQIECWg+LiIjMAINZMmvuPu6q5dZ/j/5T22XTlMX4MuOR2J6TlCIiYcKEmDVrFv777z8sWLAA8ePH13pIRERkJhjMktm66nIV/Q/1x1OPp7C2sEbvwr3RMndLVWJA33f48GHVcqtUqVJqu3Xr1mjVqhXLCoiIKFYxmCWjpJPlk/0s8MndE7oPblE6lufHL0vQ6rw84W/ph3U312Huxbnw0/khU7zUGFd6HPI65wW8PkEX1ri8vGDu/P39MXHiRNVyK1WqVLh48SKcnZ3VdQxkiYgotjGYJaMjSyff2fAROpdUwOZeUT6ev6UtUG6G+vlZhQp4qfNBYQDLAm/xRPKKuBXlR4r7Xr16hRYtWmDv3r1qu3LlynBwcNB6WEREZMYYzJLR8XTzgM4FRs2hUCFYmFkQt3//fjRr1kwFtI6Ojpg/f74qLSAiItISg1kyasmXTYZdrgpROoa3ty8w8pL6uUNPK6ROnFlN8sqROHukjymBrLmcUpdM+ejRozF27FjV+SFv3ryqW0GuXLm0HhoRERGDWTJuDvHs4ZgoQaTv7+LlgmHHhyMfGqrtH3NUx9AyQ+Bkwyb+4SVB+/Xr11Ug26FDB9W1QDKzRERExoDTtinOOvH8BBpsb4BTL08H7htVcjQD2QhkZA3B7OLFi7Fp0ya1mhcDWSIiMiYMZinOkQ4Fcy7MQec9nfH201tkTpg58DpzKQ2ICj8/PwwePFit5iXZWEMf2QYNGmg9NCIiom8wmKU45eXHl2j/b3ssvLwQeujRIHsDLP9pudbDMhmyFG2FChUwadIklYmVXrJERETGjDWzFGccfnoYQ48OxQfvD6qUYGTJkaieqTp8vf21HppJ2LVrl1r04N27d0iQIIEqLShfvrzWwyIiIgoTg1kyeb7+vph5fiZWXl+ptnMnzY2p5aYifYL0Wg/NJPj6+mLIkCGYNm2a2i5cuDA2bNiALFmyaD00IiKi72IwSybtqftTtSTt1bdX1XaLXC3Qp3Af2FrZaj00k9G0aVP8+eef6ueePXtiypQpsLOz03pYRERE4cJglkzWfw//w6jjo+Du644EtgkwtvRY/Jj+R62HZXJ69eqFQ4cOqU4FdevW1Xo4REREEcJglkyOt783pp6Zig23NqjtAskKYEq5KUgVL5XWQzMJ3t7euHjxIooXL662y5Yti4cPH8LJiS3LiIjI9LCbAZmUB64P0HxX88BAtn3e9lj601IGsuF0//59lC5dGj/++CNu3LgRuJ+BLBERmSoGs2QydtzbgcY7G+PW+1tIYp8ECyovQO/CvWFjaaP10EzC5s2bUbBgQZw7dw729vZ48eKF1kMiIiKKMpYZkNHz9PXEhFMT8Ne9v9R2sZTFMKnsJCRzTKb10EzCp0+f8Ouvv2L+/Plqu1SpUli/fj3SpUun9dCIiIiijMEsGbX7H59jyK4mqrzA0sISXfJ3Qad8nWBlaaX10EzCnTt30KhRI1UjKwYNGoQxY8bAxobZbCIiihsYzFK0kaVP9V5eUT9OkGN0vjYLbtZ+SO6QHJPKTULRlEWjfHxzsnr1ahXIOjs7Y9WqVfjpp5+0HhIREVG0YjBL0RbIPmrWHF4XLkTrcb11viiTpizGlxmv6mQpYoYPHw53d3dVZpAmTRqth0NERBTtOAGMooVkU6M7kL2ZFuictS7mVZrHQDacbt68idatW6v2W8La2hrTp09nIEtERHEWM7MU7bIdOwpLB4dI3feN1xs02dkM7j6uaO3hho5pq6haWfq+lStX4pdffoGnp6ea3DVu3Dith0RERBTjGMxStJNA1tLRMVKlCiOPTYQL3JBFB3RxdYV/jIwwbvn48SO6d++O5cuXq+1KlSqpbSIiInPAlBcZDVkI4djzY7CzskOv1/7gfPvvu3btGooVK6YCWUtLS9Wp4N9//0XKlCm1HhoREVGsYDBLRuGh60P8dvY39XO3/D2RzlfrERm/v/76C0WLFsX169eRKlUq7N+/X034srJi2zIiIjIfDGZJc746Xww+Mhif/D+hRKoSaJi9idZDMgl58+ZV/WKrVaum2m+VL19e6yERERHFOtbMkuYWX16Mq2+vIr5tfIwtPZYTvsLw+vVrJE+eXP2cJUsWnDx5Ejly5FAlBkREROaIn4CkqasuV/HH5T/Uz8OKD0NKJ9Z6hjY5bsGCBciYMSP27NkTuD9XrlwMZImIyKzxU5A04+XnpcoL/PX+qJ6xOn7O/LPWQzJKrq6uaNKkiWq75eXlhbVr12o9JCIiIqPBYJY0M/3sdDx0e6iWqh1aYqjWwzFK586dQ+HChbFx40a1AMK0adOwZMkSrYdFRERkNFgzS5o49uwY1t9ar34eW2YsEtol1HpIRldWMHfuXPTr1w8+Pj7IkCED1q9fjxIlSmg9NCIiIqPCzCzFug+fPmD4seHq52Y5m6FU6lJaD8noSJutnj17qkC2bt26uHDhAgNZIiKiEDAzS7GecRx7cqxatjZTwkzoXbi31kMySrKKV8eOHVX7rR49esDCwkLrIRERERklBrMUq3Y92IX/Hv0HawtrTCwzEQ7WDloPyWiC/N9//x2NGjWCs7Oz2rdw4UKth0VERGT0WGZAseblx5eYcHKC+rlz/s7I45xH6yEZhbdv36J27dro1q0b2rRpA51Op/WQiIiITAYzsxTtPH38YGntF2yfTq/D4CND4e7rjjxJ86FZjjbqdiHf3x/m4vjx46rt1pMnT2BnZ4caNWqwpICIiCgCGMxStJ0mNyg8bi+8re2CXW+T+CjsU56GXmeDU6eq4Yej+8I83l5bxGmSfZ06dSqGDh0Kf39/ZMuWTbXfKlCggNZDIyIiMikMZilaePmGnk21tH0Fu+S71c/er2pA7xtQExoedtaWcbKsoEWLFti9O+A1adq0Kf744w/Ejx9f66ERERGZHAazFO2ODvwRTgnjqZ99/X3Rfk8r3Hrvh5KpSmNGkxHhOo1u/8dI4C3i5Cl3Kysr3Lp1C/b29pgzZw7at28fJ58nERFRbGAwS9HOwcYSjrYBf1qzz8/Hrfc3kcguEcaXGQsnO5vwHSSOBXdSViABq1wSJUqEzZs3w8bGBvny5dN6aERERCYt7p3DJaNx8fVFLLkasPTqiJIjkMwxGczRq1evUK1aNSxYsCBwX6FChRjIEhERRQMGsxQjPH09MfjIYNXFoHaW2qiSoQrMdSWv/PnzY+/evRg2bBjc3d21HhIREVGcwjIDihFTzkzBU4+nSOWUCoOKDYK5kQ4FY8aMwdixY1Wnhzx58qhuBZzkReby9+/r66v1MIjIyNna2sLSMup5VQazFO2OvziKP+/8CQtYYHyZ8Yhva14B3PPnz9G8eXMcPHhQbcsEr9mzZ8PR0VHroRHFKPni9vLlS3z48EHroRCRCZBANlOmTCqojQoGsxTtppyZpP5tlbsViqYsCnPi4eGBIkWK4MWLF3ByclIttySwJTIHhkA2efLk6ssbu3QQUVgToyX5I5+X6dOnj9L7BYNZinYfvN8ha/Js6FGoB8xNvHjx1LK0mzZtUmUF2bNn13pIRLFWWmAIZJMmTar1cIjIBCRLlkwFtH5+fqrDT2RxAhhFO2tLa0wqOwl2VsFXAYurnj59ijt37gRuDxo0CCdPnmQgS2bFUCPLchoiCi9DeYF8GY4KBrMULV58fBH4c7s8nZAjSQ6Yg127dqklaOvXrw8vL6/ARRFkQQQic8TSAiKK7fcLBrMUZf46f0w8PSZwu3GOZjCHLFT//v1Rs2ZNtTytnB559+6d1sMiIiIyOwxmKcpWXF+Byy6XAretLOL2n9WjR49Qrlw5TJs2TW336NEDx48fR5o0abQeGhERkdkxiqhj3rx5yJgxozo1W7x4cZw+fTrU2y5atAhly5ZF4sSJ1aVy5cph3p5i1q13tzDnwhyYi7/++kuVFUhNbMKECfHnn3+qtlt2duZRH0xEMW/JkiWoWrWq1sMwGSVKlFDvxWS+NA9mN2zYgL59+2LkyJE4f/68Wi1Jlv58/fp1iLeX3p1NmzbFgQMHcOLECaRLl079T//s2bNYH7u58/b3xuCjg+Gn80Pp1OVgDm1EJBsrM7aLFi2KCxcuoF69eloPi4ii6M2bN/jll19UeyD5YpoyZUr1OXTs2DH4+PjA2dkZkyYFtBz8miyMkiJFClV6tHz5clUDmCtXrm9uJx1O5DpJ3ITl06dPGD58uPpMDGmyqUyYyZs37zfXPXz4UB3/4sWL31xXoUIF9O7dO9g+ef9q2LChGrskkrJly4aOHTvi9u3biCx5jjlz5lTHk+W6//777+/eZ82aNepzXyYOpkqVCu3atVOlWxE5rqyuKBNv5T2azJPmwez06dPV/0Bt27ZF7ty51fr18ke9dOnSUP/wu3btqrJj8se9ePFi9Qe8b9++WB+7qZPXzeODW6Qvs4/9hsevbiOFRWJ0y94d5tDcee3atRgyZAiOHj2qGj0TkemTCZwS3K1YsUIFc9u3b1cBoARVEjy2aNECy5YtC3GRCAlgW7VqFdhWSPpLSzJGki1fZ1slWP6ezZs3I0GCBChduvQ318ljNWrUCG5ubjh16lSkn+/OnTtVNtPb21t9pt64cQOrV69WZ5skkI4MKbWSRJMsEiOvZd26ddXl6tWrod5HvizIayf3uXbtmgpa5UyrxAQROW716tXVUuH//PNPpMZOpk/TPrPyjffcuXMYPHhwsIBBSge+fiMIjaenp/pGnCRJkhCvl/9Z5WIgbwIUEMj+V6kOMry4G+lj1Px8Ad7AH/WjPii9Hnh0DDj5O+DyudWVhRW0JB8sly9fVkvTCjkTMH78eE3HRGQqJNjz8o1ay53IcrCxCtdMaTnTcuTIEXXWr3z58mpfhgwZUKxYscDbSCA1a9Ys9SW2TJkygfsPHTqE+/fvq+sNrK2t0axZM5WQKVmyZGBGVY7fp08frFu3LszxrF+/HrVq1QrxtZSAev78+UibNq0KjqUsL6LkM1OSRz///DO2bt0auF++nMvxIrt6m7w+P/30k5oYa8hY79mzB3PnzlVJqpDI57xkqnv27Bk4hs6dO2Py5MkROq50kJHnI69djRo1IjV+Mm2aBrMuLi6qt5ic5ghKtm/evBmuYwwcOBCpU6dWAXBIJk6ciNGjR0fLeOMSTzePKAWyoXmUKityJIgXsTv5fgKubAJO/QG8uvJlf7ZqQKr80IKc6vv111/VB4eoWLGiuhBR+Ekgm3vEv5o89vUx1eBoax2uhU7ksm3bNpWtDKn+XU5tS2mRBKhBg1kJLkuVKqXOEgYlp8olsyuBmJxplIyqBGRff9aFRALmli1bfrNfSuskEJXPOplsKo87Y8YMlQmOiH///Vd99g4YMCDE6xMlShT4s7wuYZGMtSGglMBUSgaDklINeV1DI8G+nOmSsgHJrkpGWxIIEpgahPe48uUjtFIQivtMegUw+cOVb2LyjTe0vp6S9Q36P4JkZiW7Rl8k/W8/HBOE/w1x0plx2P3wb6RySo0lVVfC0fpLk3QJZCW7Hi5uz4Ezi4FzywHPzzVS1g5A/iZA8S5A8uAfELFFFkBo3LixOqVl+MIU9AOMiOIOyaRKsCmntiUwK1SokMrQNmnSBD/88EPg7ST72q9fPzXhU4I8Oa0tgZdsf61gwYLInDmzul4CUzm+lNRJFjcskhV1dXVVCZqvSSZWxiRZSKmZlePLafk2bdpE6PkaFnj5OgAPSUj1t0FJOUTQpYxDSkzJ/tBIKYWUOcj7rSQQZBUoyUrLpPCIHldesydPnqizjuH+DKI4Q9NgVorq5X/MV69eBdsv21KAHxaZiCPB7N69e4O94XxNvmVzpnnYJJCNl+jLm1JY9j7ai7+e/wNLOyuMqTwZyZ3D/j2FWErw9AxwagFw/S9A5xewP2E6oFhHoGBLwDHkkpHYIKcAO3XqBA8PD/X3uWrVKpVRIaLIneqXDKlWjx2Rmlk5PS3lBtKpRGovp0yZouZkGIJFqduUMgFZployrzJ5WYImCcRCIreRzK3UyX78+FFlG+XUeFgMC698nZyRIHfLli0qaxs0KyoBbkSDWSlXCK+sWbMiJl2/fh29evXCiBEjVLb1xYsXqpygS5cu6rlFhIODgwpkpaxQfibzomkwK4X1hQsXVpO3pKBbGCZzde8e+oQieZORukU5XVKkSJFYHLF5c/FywegTASUb7fK2Q8HkBcN/Zz8f4NrWgCD2+fkv+zOUDsjC5vgZsNL2RIGUFUj2REgfWZnsxd6xRJEnNavhOdVvDCSArFKlirrIJKgOHTqojgKGYFGykA0aNFABqiFQlclYoZ2Kb968uTqVP2rUKJWdlQzw9yRNmlS9Zu/fvw+2X96LJHMZtEZWglL5vJQJa7J0tiFLKpndr0kwLJO7hGGZbSnlM9T0hiYiZQaSgIpoYkrKACU7a6iHlcSUlE1I+81x48ap7gbhPa4sWiP3ZSBrnjTPxUsJgPSOlVmkMqNS2qPIt1gpUBcy0zHoBDEpDJc3GqldksJxOdUgF8mkUcyRN84Rx0bgg/cH5EySE13zdw3fHT1eAwcnAzPzAls7BQSyVnZAgRZA58NA27+B3LU1D2SFfFDIB4m0eZEvVAxkicyXdNeRz6KgpNRAsqPSDUBm2Qed+PU1mZRcu3ZtNUlMgt/wJnjkcSVjGZRkKeXLtpz2N1wuXbqkgj5D5x95PDmbJJOqg5LSurt37wYGsdLKUm4nSaGQBJ0AFvTxQroYJsYKCYy/7iokE7XCCpilBvjrkgA5Wxs0gxze40p3AynvIDOlNwJz5szRp0+fXm9ra6svVqyY/uTJk4HXlS9fXt+6devA7QwZMshf+DeXkSNHhuuxXF1d1e3lX3Pm/t5Vfz1HTnWRn79nw80N+rzL8+oLrSykv/Puzvcf4NkFvX5LZ71+jLNePzJBwGVqdr3+4BS93uONPjb5fPLTz+28T13k56BevnwZbPvGjRuxOjaiuMLLy0t//fp19a8pcXFx0VesWFG/atUq/aVLl/T379/Xb9y4UZ8iRQp9u3btgt1Wp9Pps2bNqk+cOLE+Z86c3xxr2bJl+oQJEwZue3p6quMbzJgxQ32GhaVv3776+vXrB25fuHBBfWaF9N40f/58fcqUKfW+vr5qe8KECfqkSZPqV69erb97967+1KlT+po1a+ozZsyoxmKwbds2vY2Njb5WrVr6PXv26B88eKA/c+aMvn///vrGjRvrI+PYsWN6a2tr/bRp09RY5TNZHuPKlSuBtxk0aJC+ZcuWwV4vuY88j3v37umPHj2qL1KkiIoDInJcQ6wwZsyYSI2djPN9IyLxmlEEs7GJwWzEg9lHro/0RVcXVcHsiqsrQr+hn69ef3WLXr+46pcAVi4Lf9TrL2/S63299VoIKZj18PDQt2nTRn1gvXjxQpNxEcUlphrMfvr0SQVZhQoVUoGoo6OjPkeOHPphw4YFCwANJGCUz5ApU6Z8N5j9WniC2WvXrukdHBz0Hz58UNvdu3fX586dO8TbynuXpaWl/q+//lLbfn5++tmzZ+vz5cunnkfatGlVcCrB6tckeK1Xr54+WbJkejs7OxWkd+rUSX/nTjiSFaGQLwHZs2dXiak8efLod+3aFex6SUxJ0BmUjFeenzznVKlS6Zs3b65/+vRphI4rt5cA98mTJ5EeO5l2MGsh/4EZkVMuUjskdUVBZ2KaG1n04EmJgPqrdCdPhToBTFb3ar27NS6/uYxiKYthUdVFsLT4qjrF811ARwLpTOD2eSU2S2sgz/8C6mHTalvX7Ovtj4W9DqmfO80qj9t3b6paNzmVJ6e4Vq5cqerbiCjypKbzwYMHqldoaN1lKHxkZS7pqhC0xI5CJx1npM544cKFWg+FovF9IyLxmvaFimTUllxZogLZeDbxMK70uOCB7KtrAb1hL28E/AJm4cLRGSjSLuCSIBWMScBqPcvQq09PNWtYJhfIxArpB0lEZCymTp2KHTt2aD0Mk5E8efJvetGSeWEwS6G69vYaFlwKmKk6pPgQpIqXCtD5A7d3B3QleHD4y41T/gCU+AXIUw+wMb6sjLevF9YfnoEzC/cFToKQtlvyJkhEZExkcnOPHj20HobJkMlxZN4YzFKIPvl9wuAjg+Gn90PVDFVRM3VZ4MQ84PRC4P3DgBtJljZXrYBSgvQlpQ8PjNXu86tx5u4+NVNWlkOU01JsrE1ERGT6GMxSiGaen4kHrg+QzC4xhrv7wmJ6bsD3c5sa+0RA4dZA0Y5AItNYTe2nQi3w+M1tLFg5HRV+DFh/nYiIiEwfg1n6xvGnx7Dmxhr185hHt5DI61LAFclyAcU7Az80Bmy/LGFrjKRwXPoXG+qo7Gwc0KPmVJQuzWVpiYiI4hIGs/SFtwdczy/D8JsL1XIajd3cUcbLG8heHSjRBchU3qhLCQzOnz+vlpiURuGiZ/feWg+JiIiIYgiDWYLFh8fAqXXA+VUYn8Aar+M5IYOfP/pmbQg0/wVIkhmmQLoVzJs3T00G8PHxUWuiy1KJREREFHcxmDUxErB5+fpH+ThePl+OYb+4LGCtwz9OjvgnXkJYwQITqi+FY+oSMBWyBKMsLbllyxa1XadOHbXMoyzxKH1miYiIKG5iMGtigWyDBSdw7tH7SB/DDj6oa3UMbfT/BO6zgB6vMpfHWMuXgL8XOuXvgh9MKJA9e/asajL+8OFD2NjYqB6NPXv2hIUJlEQQERFR1LA3kQmRjGxkA9lUeIsB1utx0q47JtssQg7Lzyt1AfDvsB/DU6aEu78X8ibNi44/dIQp0el0ePr0qVpB5NixY+jVqxcDWSIyGfJ+tW3bNq2HYXLevn2reoVLIoO+b9CgQXG2fzGDWRN1dlhlXB9TLezL6Kq42TER7hbYgOMOvdHVejsSW3hAlzA9fCoMCzzW5veXcOLFCdhb2WNC2QmwsbSBsfP3/1I6UKxYMWzdulVN/CpatKim4yIi09OmTRsVUMpFzu7IF+MBAwaopTbN5XkHvRgmz2o1prp164brtuPHj1clZbLIxNeqVaum+oqfOXPmm+tk1cfevb+dGLx8+XIkSpTom844Q4cORc6cOdVyqylTpkTlypVVSZucLY2Mx48fo0aNGnB0dFTBeP/+/eHn5xfq7Q8ePBji70kuhuf38OHDEK8/efJk4HH69euHFStW4P79+4hrWGZgohxtreBoG8qvz88buLolYJWuFxe/7M9YVi1wYJmjOmw/ectitWr33AtzASugb5G+yJQwE4zd8ePH0bZtW2zcuBH58+dX+2rWrKn1sIjIhP30009YtmwZfH19ce7cObRu3VoFA5MnT4Y5PO+gkiVLFqljycRbW1tbxAZPT08sWbIE//77b4jBonxOdO/eXc2diGySQ+ZilClTBq6urhg3bpw6jrW1NQ4dOqS+7Pz444/fBL/hScRIICtBsYzxxYsXaNWqlfoSNWHChBDvU6pUKXW7oIYPH459+/ahSJEiwfbv3bsXefLkCdxOmjRp4M/Ozs4qyP/9999VOV5cwsxsXOL+CjgwEZiRF9jWJSCQtbYHCrYEuhwD2uwEctUELK2C3c3b3xulU5dGkxxNYOzlBFOmTEG5cuVw+/ZtDBkyROshEVFYJHPl81GbSwSzZnZ2dirASJcuncoMSvZtz549wU5pN23aFGnSpFEZtXz58mHdunXfZPykXl8CHZl8KscbNWpUsNvcuXNHvYdJli937tzBHsPgypUrKlBycHBQwUinTp3g4eHxTfZSgp8UKVKogGrMmDEquydZPnnstGnTfhOkhvW8g14koykkaJMzX3KbVKlSqdPUQTOI8nwlYJQspyFQElevXkX16tURL148Nb6WLVvCxcUl8H6bN29Wr5/h+clr/fHjR/VaSebwr7/+CswsSlYyJH///bcaV4kS387vkOctCY5ffvlF/Y68vLwQGfIZIxnPU6dOqS838vvKnj07OnbsiIsXL6rnF1H//fcfrl+/jtWrV6NAgQLqdZJVKaUTj3wZCIl8QQj6+5HXTF4jSep8XVKXNGnSYLeVIDmoWrVqYf369YhrmJmNC56dA079EZCN1fkG7IufGijWASjUBnD68s0sJPFtE2BM6TFGXWf65s0b9Wbyzz8BE9eaNGmCP/74Q+thEVFYfD2BCam1eewhzwFbp0jdVYIxyZplyJAhcJ+UHBQuXFgthZ0gQQLs2rVLBWlZsmRRAZ+BBGOyWIsEQCdOnFCBp7QIrFKlivpCXq9ePRXgyfWS8fv6dLcEdRIUlixZUp1Cfv36NTp06KCCRjkNbrB//34VsB4+fFjNFZBuLjJmCZTl2Bs2bEDnzp3V48rtIurZs2f4+eef1fhXrlyJmzdvqiBOgvCgAbo8XwkaZQyGbKYE4jLmGTNmqEBSXrNGjRqpMUuGUb4USGLif//7H9zd3XHkyBF1yl5Og9+4cUOd2jcE4hKYh0TuI7+Pr8lx5L4SHEppQNasWVXwLL+riJDflQR9zZs3R+rU3/4NBw1ku3TpooLTsBi+jMjfhATy8jdgIL9veQ2vXbuGggULfnds27dvV1+uJJj9Wu3atdXfqgTd8qVKtoOSv1WZYyJBekjlGaaKwayp8vcFrv4FnFwAPD39ZX+64qqUALlqAVah175ecbkCw8mgQcUHIrljchgredOS4PX58+fqjXT27NnqjdKYg28iMi07d+5UAYpkHr29vWFpaYm5c+cGXi8ZWQm2DGQijZzilnKnoMHsDz/8gJEjR6qfs2XLpo4hp4MlqJRTwBIUyv0MAZJkVyU7Z7B27VoVjEgA6eQUEIzLMSSjJiUPhiBIgjx5L5Rx5siRQwWHcurdcMZq8ODBmDRpEo4ePareP7/3vA1kLJs2bcL8+fNVlloeW95rJTCU92AJTEeMGKEe1/Ac5bEN5HS8BGRBT5nLqX45lpxRk6BOXmMJ6g1fFiS4M5Bsrbz+klUMy6NHj0IMMuU1ltfBkCVu0aKFKkeIaDArmeT379+r5/09khUP+rcRlpcvXwYLZIVhW64LD3k+8vyCfkmJFy8efvvtN/XFSX43f/75p8rey8TCoAGt4TWT14/BLGkmMdzQ1Go/7Of/Crh/rqGRCVt56wcsNZum0HeP4enriVHHR8HwVlMlfRUYK3kjllNZ8i1Z3rDlg0M+LIjIBNg4BmRItXrsCKhYsaKqJZTMqGQUpTayfv36wWodJUCT9yDJWsopYQm6pOQgqK/fn+T0vGRXhWQdJagLGoRJBjYouY3MBTAEskICFHkPvHXrVmDgI3WRhoBSyP68efMGbkupgJxyNjz29563geFxZRwytqBJAxmHBKOS2ZNFacTX2dFLly7hwIEDIZ6Cv3fvHqpWrYpKlSqpAFYCMtlu0KABEidOjIiQjK8kN74mgbOsACm/PyFZYCm9kMeWLHp4RWRyl0zikktskNfe8CUqKGdn58Dl24XU98qXD6mNDRrMypcFIQF/XMJg1lS8vArbE/Nx0m4j7Cx8AXd510kOFGkXcIkf/JteWH47+xueuD+BKZA3U3mzlTd/yRREpkaJiDQigVAkT/XHNgni5JS0ISCSgFIyYHL6XkhQMGvWLMycOVMFYnJ7KRH4us7x6xpFCQYlEI1uIT1OZB476POOjKBBt5Bg15BF/poE9hJkS52wlERI/eicOXNUtwApjZAuEuElwZtkToN69+6d6mwjk/iCBujyRUR+p9L9QEiZiJR4fE1KJBImTBg4CU5qkSWT/j0RKTOQjPPp00HOpgJ49epV4HXfIyUU8iXl6/KBkBQvXvybmmx5jaIyyc9YcQKYMdP5Azd2AMtqAAtKw/rSGhXIXtZlgnet+UCfq0DFwREKZA8/PYyNt4N/ozM2UntlKNiXN74dO3aoU24MZIkoNkjGU07XDxs2LPC9SN6XpA2UnLaWQDdz5szqtHlE5MqVC0+ePAk2Mz1o6yTDbSS7KRliA3lsQzlBbJFxSH1n0AyljCN+/Phh1uAWKlRI1X7KKWwJkoNeDIGvBNmS5R09ejQuXLigJjhJECrk56CtF0MjpQwykSqoNWvWqLHJ6ycTtAwXOf0u9caG48rrKK0cvyb7pNZUyOst5RlyTMlwfs1QLmEoMwj6eCFdgiZoZIJf0Iy5BJwSYMsEs7AY6oEN3Q++5+LFi+oLxNf14HLfoB0P4gIGs8bI6z1wfA4wuwCwoQXw6ChgYQW/XHVRz3sUavuMg3++xoC1XYQO+/7Te4w8HlDL1SSn8XUukDcamVhQtmxZ9OnT55vTIkREsUVWFZQv0zKRyFAbasgoyil4mVxlyKiFl8zal2BJJrNKwCXzASQrGZRMOJLT53IbCTzklL3U50rN59e1ljGpa9euKvCWx5bspMyel1pgOZUdtLzha926dVPZPzm9LxPY5PS+nBaXyUryHi8ZWCnXkJUbpYWW9GuVCb4SPAsJgi9fvqxKKqRuVbKsIZESBQmag2ZnJZMuJQtSbhH0Itl1Odbu3bvV7WSylXwRkc4ThseaPn266nzw66+/Bh5PMrlSFiIZTkmoSPAs3SgkyyvBtCHbKiUGXwfuX18MpKxCglb5fcrfgLw28qVJXjfpziAkcyu1ulLOEpRMoHvw4IGaM/K1FStWqPHL70ou8hrLOL9eJEH+5uQzNq59rjKYNSZvbgE7+wLTcwP/DQM+PAYckgBl+gK9L8Pnf0twXi/fGiM+8Um+0Y05MQYuXi7InDAzuubvBmMimQp5o5dv6jJWeQMLz7dzIqKYIDWX0kFAJjdJllQCDsk6ShAldfxySji8zf0NJAiUDKRke2XSmAQlhlPfBlKDKwGOBIRS9yjBmdSYBp2MFhtkwpu0v5LASjLRcipdgkJ5HcIiJWGSwZX3bwncpCRDyjHklL08f8lASgcG6ZQggb0cTzKnhklw0jFBMqfSP1VOhRu6JHxNjiu/D0PtqPQGluAwaJ2zgZQOyGsowa6QrLqMQYI++dyRYFWOIxPfpO+ugUyyk8y5ZOMNE9skEJSgUcpODCUJESFfkGTSnfwrWVo5tmRaJbtrIPWsEmB/HcjL+KXnbGiT0saOHatqmOX5yJcP6WjxdccD6dAgr3FcY6GP7BIWJkpafsgfoNTLyP9UmpN6prt7gVO/A/f2f9mfPA9QoguQryFg87lg28cPuUcENIiWFb5CXTQhBNvvbcfQo0NhbWGNNTXWIKdDRtwqFFC4n+P8OVh+NYkhNkndlPwPLd/O5TTUggUL1HZ08fX2x8Jeh9TPnWaVh41d8D67RBR1MgNfskZS9xjSxByi6Cbt0WRyl2Sww8oWUwBpbSmZZ8lGGybIGfP7RkTiNeN4NubI2x24uDagP+y7e593WgA5awR0JZDVuqKp9dRzj+eYcCqgd0HXAl2RO2lu6IxgJqPUG8lpq4kTJ6psrMwClm+S4WmFQkRE5k1W0pLT/nI6XsoBKGxyhkFqbo0lkI1Oce8ZGbt394FTC4ELqwEf989LsCQECrUEinUEEkdv3zedXqcysh99P6JAsgJom/fbJstakQJ4ycJKICv1Z9IOJ67V8RARUcz5etEJCp2UrMRVDGZjg1Ry3D8InFoA3JYygc+VHUmzBWRh8zcF7MI5U18P2Hw+de6r/37mds2NNbj4/DLiWyfAmGLjoPe1gC/8ofP2h79lwLIJcixLq9ivT02WNAWWLlkGd3cPNG7UOHAs0S0mjklERETGgTWzMe3lFeDPjsCbG1/2Za0SUA+b+UeZERDuQ3309sX4vgeRxt80az79/f2w48xSZEmZF/kyltJkDKyZJYoZrJkloohizaypOL8qIJC1jQcUaAYU6ww4R65BtZ+PzmQD2Xfur7Bs3zg8eHUdx2/+jVFNV8MxvNnoaJIqS0JY23KSABERUVzCYDam6T+f4i7ZDagYsGZ2dGgyvgQSxAu9z+zcC3Ox8vpKJLJLhHU11iGpQ9Jg18sEsDuly6ifsx07GqPdDHbs3IHhHbupfoDyLWvhH4vxv7o1ENskkA26NCMRERGZPgazsSZ6gyhrW6tQT5efe3UOy24tgd5Kj2FlhiBlom/XjNb5W8FKF7AMoxzHMgZOvcsyjwMHDlTLPwrpmSjdCiKyZCERERFRWBjMxjEePh6qe4EeetTNWheVMlTSZBzS9Fkai8sKMEJW9Jo0aZJaqpCIiIgourCAMI6ZcmYKnnk8Q5p4aTCw6EDNxiGr2MhqKYkTJ1YrkchSgQxkiYi+f0ZLlj+VZXPp+2SJ2gIFCkAnCxCR2WIwG4fse7wPW+9uhQUsMK70OMSTSWexPCtRlmA0kPKCixcvonbt2rE6DiKiiGjTps03S9Nu3rxZza6WpVYNt5GaeznDFNS2bduC1eIfPHhQbefJk+ebJbllSdfly5eHORbpvS2lWLJs6dekH7csgyrLrobnOQQdz4cPH4IFzLJMryxTK4kHZ2dnlC5dWjXU/3oJ1fCS9/7mzZurWefyPGXpWw8Pj+/e78SJE/jxxx/V6o9y33LlyqnlfsN7XFl+1sbGBmvWrInUuCluYDAbR7h4uWD08dHq5zZ526BIyiKx+vh3795Vb76NGjUKfAOXBRDSp08fq+MgIoqqxYsXqwDq999/V8t/GkhwO3nyZDWZ9Xvu37+PlStXRuhxpVPm3LlzVcAWUunW+vXrMWDAACxduhSRJYFstWrVVFDeqVMnlQE+ffo0unXrhjlz5uDatWuROq68XnLfPXv2YOfOnTh8+LA6/vcCWQlGq1atqsYgZWndu3cPtjRteI4rgfzs2bMjNW6KG1gzGwfIG6AEsu+93yN74uzoXqB7rD6+vMHKm4u7uzuSJk2Ke/fuIXv27LE6BiIyzvcmL78vWbbY5GDtEKnuJZKxlGW25X3tf//7X7DrKleurL64yxLccruw9OjRQx2nWbNmsLMLvfNMUOfOnVPvn7JM69ckG5s7d24MGjQIqVOnxpMnTyK1hKucMZOA8OzZs6oUzCBz5sxo2LChCnYj6saNG+p0vwSjRYoEJFIkMP75558xbdo0Nd6QyFyKnj17qudkkCNHjggft1atWioIltcuS5YsER4/mT4Gs3HAljtbcPDpQdhY2mBCmQmwtYqd2lQ5FSRLCS5cuFBtlylTBuvWrUPatGlj5fGJyLhJIFt8bXFNHvtUs1NwtIlYy0HpvjJ//nyVAaxU6dvJs3KKf8KECSpAlSAsrPc6eW9cvXq1Cr769esXrsc/cuSISgTEjx//m+uWLFmCFi1aqPaG1atXV+UKw4cPR0TJ6XgJyoMGsgZyul4uQp6nXMJy/fp1dfZNMqxSAmAIOIU8hmRYT5069c2XAsNy5nKdZF7lrJ4Eojlz5sT48ePVZ4kI73FlDClSpFCvH4NZ88QyAxP3xO0JJp+ZrH7uWbAnciT58q02Jt26dQslSpRQgaxkP4YOHYoDBw4wkCUik/TPP/+obKtMWA0pkDWQAEomHEnWNSxSiyq3kSyurGAUHo8ePQoxi3nnzh2cPHkSjRsHLPstQa3Ut0ZmAU85lgSN39OlSxc15yGsi2GsL1++RPLkwVtAWltbI0mSJOq60MowxKhRo9CxY0eVgS1UqJB67WWMET2ujEVePzJPzMyaMH+dP4YcHaKyH0VSFEHL3C1j5XHlDVS+TV++fBnJkiVT3/SrVKkSK49NRKZDTvVLhlSrx46IH374AS4uLioALVasGOLFC30CrdTNyqSl72VcpfZVJpDJ7b+X5TSc7QppKWCpkZU6V5moJeQ0uxx7//79YQbeIQlvACwBo1xiiqH7gExqa9u2rfpZssX79u1Tz1e+BESEzNGQumIyT8zMmrBl15bh4puLcLJxwvgy42FlGTtL3UomVk55yamuS5cuMZAlolDfK+RUvxaXiNbLpkmTRs38f/bsmZqUJHMAQiMz7iW4HDx4cJjHlCyinDafNWsWnj9//t0xSLD69eQymVC7YsUK7Nq1Sx1PLpL1lVn+QSeCyWz/kDLA0sVAyiOkW4CQMoabN29+dywSfEtAH9bl8ePH6rYpU6ZUZQNB+fn5qTHKdSFJlSqV+lfqgIPKlStXpI4r+yS5QuaJwayJuvP+NuZdmKd+HlxsMFLHC7nAPrrIbFKp/zKQli5///134BsSEZGpy5AhAw4dOqROYX8voJVuADt27FB1nWGRSVXSpmv06IBuM2GRzKQEmkGzp/I+K+O4cOFCsFP8Mj9hy5YtgS23ZOKUvE97e3sHO+b58+dVqy9DLazU++7du1cd72vSluvjx48RLjMoWbKkGodMYDOQrLFkX4sXD7lmOmPGjOr+UrIW1O3bt9XvISLHlbaQUnMbUh0wmQcGsyZq/KlR8NP7oXL6yqidJeb6uMqbqtRmyVK07dq1U+1TiIjiKukQIBlayQhK9tXNzS3E2+XLl0+VW4WnJZQEvpJFNQSKoalYsaLqoRq0PZacBZPuBpJAyJs3b+BF2iDK5ChDf1UZi2SjW7VqpYI/6bogjyndC4K2F5OJadJTVsoT5s2bp86uSf3qxo0b1TwIQ72qlBjI4g1hXSRLbMimSvAvta/yGXHs2DHVXaBJkyaBAa9kvKVW1/AZImPt37+/ev2kp6+MVya0STBvaE0WnuMKqSeWjhES/JJ5YjBroh66P0RS+6QYUXJEpNrPhIe8qbZu3VoFsVLLJcvTyrdpIqK4TCaySkArNbRhBbRjxowJ18pTUl8rFzlFHhZpbSgTzAwB6qtXr1R5Qf369b+5rczol9tKsCsksJXZ/JJdlYVqZJKaBIqy+qLUpRpI0Cc9W6Vf7R9//KECWElWyG2lQ4MEypEhY5ZgVYJkqemVjgSGTjdCxiVZ2KB1rRJYS6mGtOiSYF3qZWVsQTsSfO+4QrLUEsxL+QWZJwt9ZKZDmjB5U5LWJlJbJDVGMW7Xr8CZxUD5QUDFsOurvsfV3Rur+x9TPy8u1h+zqs5AubTlInUsnacnbhUqrH7Ocf4cLL96E5DJXfLNX9585E1z7Nixqhdg0GbWRERBT/U+ePBAndIOaRIThY+898o8BDltHtYkNAogXzikxEL65srfHsWd942IxGuMTEyIp++XU1S1MteJdCAbntVvZDavBLKGSRFDhgxhIEtEFMOkq4J0P5APePq+hw8fqt7ADGTNG1tzmZDVN1bCET+on7v+0CPGHke+BckkAulWIMsxGtrBEBFRzJPlWSl8ZEGFoIsqkHliMGtCiyNsurMerT8Hsw4RXNnme6SWy1DM37dvX7WiitRpMRtLRERExoyRiomYenYqfHW+0X5cKZme/8cf6putTPgSMqFM2skwkCUiIiJjx2jFBBx/fhwHnhyAlUX0Lorg5u+PPs+fo0ffvqo9i2FWLBEREZGpYDBr5Px0fphyeor6uU6WetF23DPnzqHBo4f4z8NdNdOeMWOGastCREREZEoYzBq5jbc24p7rPSSyS4S2eQIaSUe1rECaaJetVAlPfX2RxsYGR/btU/3+YqpfLREREVFMYTBrxD58+oB5FwOWrO1eoDvi20a9L+64ceNUg2ppYF0lXjz8mSEjihYO6DdLREREZGoYzBoxCWTdfNyQLXE21M/+7QowkSHLAkqngjnTp2Nm6jRIYBW9dbhEREREsYnBrJG6/f42Nt7eqH4eVHQQrC0j10VNllqU5QENUqZMqRZD6Nq5M8sKiIhiiSwFLiVekbV8+XK1ZC1F/2sbES1btsSECRNi5bFMnY+Pj/rdyOpsMY3BrBGSulaZ9KXT61AlQxUUS1Us0sv81apVC1WrVsXGjQGBseBSk0REwRcpqFu3bow+xpkzZ9CpU6dIB2eNGzfG7du3oxQMSwJDLtJ2MVWqVOqYjx8/hqmLyGsbFdL15++//w5xsvS6detgZWWFbt26ReiLiPw+tm3bFmzfn3/+iQoVKqilXGVJY1kVbsyYMXj37l2kY4oRI0ao37mDgwMqV66MO3fufPd+z549Q4sWLZA0aVJ1v3z58gULTLds2aLiC7lensfFixeD3d/W1hb9+vXDwIEDEdMYzBqh/U/249TLU7C1tEXfwn0jdYwjR46gQIEC6n88Ozs7eHp6Rvs4iYgofJIlSwZHx8gvdiPBRPLkyaM0Blnf/sWLFypIkYBJztJJT/GYJnM0jPm1Da85c+ao10sCzK9Ja8sBAwaooPbTp0+RfoyhQ4eqLxlFixbFP//8g6tXr+K3335TgfSqVasidcwpU6Zg9uzZWLBgAU6dOgUnJydUq1YtzHG+f/8epUuXVt2OZBzXr19X40icOHHgbT5+/IgyZcqo5ZdD07x5cxw9ehTXrl1DjNKbGVdXV708bfk3Vuzsq9ePTKDX758Qrpt/8vuk/2nzT/q8y/PqZ52bFey6D26f9HM771MX+Tkk/v7++vHjx+utrKzU88yePbv+0qVL397u40f99Rw51UV+JiKKCi8vL/3169fVvwY6nU69v2hxkccOr9atW+vr1KkT6vUHDx7UFy1aVG9ra6tPmTKlfuDAgXpfX9/A693c3PTNmjXTOzo6quunT5+uL1++vL5Xr16Bt8mQIYN+xowZga/LyJEj9enSpVPHTJUqlb5Hjx7qOrmfvHcHvYhly5bpEyZMGGxc27dv1xcpUkRvZ2enT5o0qb5u3bqhPoeQ7j979uxvPg+3bdumL1iwoDpmpkyZ9KNGjQr2XG/cuKEvXbq0uj5Xrlz6PXv2qGNs3bpVXf/gwQO1vX79en25cuXU7eSxxaJFi/Q5c+ZU+3LkyKGfN29e4HG9vb313bp1U6+fXJ8+fXr9hAkTvvt6ff3aikePHulr166td3Jy0sePH1/fsGFD/cuXLwOvl2Plz59fv3LlSnXfBAkS6Bs3bqx+j6Hx8/NTr9/OnTu/ue7+/ft6BwcH/YcPH/TFixfXr1mz5ruvvUHQ1+7UqVNqe+bMmSHe9v379/qI0ul06jWdOnVq4D4Zp7zG69atC/V+8jdepkyZcD2G4Xd+4cKFEK+vWLGiftiwYeF+34hMvMblbI3Mquur8NTjKZI7JEeHfB0idN/Xr1+rUwKGGln5+ffffw/xWyQRUUzTe3nhViFtuqXkOH8OFtGQrZMs5s8//6xKEVauXImbN2+qibRSrjVq1KjAJcCPHTuG7du3I0WKFOqU7vnz59XZsZBIVlR6e69fvx558uTBy5cvVebNcOo2f/786rS5PE5odu3ahf/9738qkyfjkvpEORMXkc+LrVu3qlPjcjGc0WvVqpXK4pUtWxb37t0LPH0/cuRI+Pv7q3IMmUQsGT53d3f8+uuvIR5/0KBBKpNXsGBB9VqtWbNGvS5z585V+y5cuKCen2QJW7durR5TXj8piZPjP3nyRF2+93qFNE+kTp066nPv0KFDaql2OfUv2c6DBw8G3k6em5ze37lzp8pCNmrUCJMmTcL48eNDPO7ly5fh6uqqVsv82rJly1CjRg1VFiCfu5KlbdasGSJKXiMZd9euXUO83lCqIL+n6tWrh3msP/74Q2VFHzx4oF4vKS0wkHEWL14cJ06cQJMmTUK8v/wuJHsrmWh5HdOkSaPGFdbfZGiKFSumxhyTGMwakdeer7Hw8kL1c+/CveFoE7E34tOnT6tAVk5HzZs3T735cpIXEVHkzZ8/H+nSpVNBmLyf5syZE8+fP1d1gBKcyanWFStWYO3atahUqVJgcJM6depQjyl1qjIZVwIMOY0rwZt84IskSZKo4DJ+/PjqNqGRoEsCkdGjRwfukyA4LBKMSbAkCUFD6ZnUf0pAKeRYEoRKcCkyZ86MsWPHqtPnEszK54sEgRIUGsYm46hSpco3jyW9y+vV+7LQj9xfglvDvkyZMqlT1xJ0yePJa5ItWzZ12lpe5wwZMoTr9fravn37cOXKFRXEye9NSLAvQbDU1srpe0PQK7Ws8jobJnbJfUMLZh89eqR+L1+XehiOIyUIQn4nEuDL48tzjAipY5XXXJ5jWCSg/ro+9WvypUpIIBt0O+j1hutCcv/+fZUMky9qQ4YMUa+d/K1IHazh7yO85P8Fef1iEoNZIzLr/Cx4+Xnhh2Q/oEbmGhG+f82aNdWbhXybkv9xiYi0ZOHgoDKkWj12dLhx4wZKliwZLDEgtYQeHh54+vSpyupJTWjQ4EoyXzly5Aj1mJLtkgleErj89NNPKvMrk3WtrcP/kSzBTESzZBK4ScZYxit1kJIJDBq8SbZTMsxB90k2VmorJfiVGlsJEIMG2aEFlUEzmBLwSxDcvn37YGOWrKm8VkKSLxIUy+smr4l8nsnkooi+XvL7kjEaAlmRO3duldWU6wzBrEyyMwSyQiZHSbY6NF5eXmr+ydcJIgnw5fnJmISzs7N6HkuXLlVfBCIioOrg+yRhlTVrVsQknU6nfoeGzg2STZf6Xam7jWgwK+ON6Xk7DGZjmPxt+unsAD9LwNs/1NtddbmKv2/vhjVs0T//QPj76OGP4Lf38/myrfPyxLPXT9GzXz9MnzwZ6dKmVft7d+kScP13/nB0Xl5RfGZERGFTs+djYWKOqZFASwLDvXv3qmBITt9OnTpVnc79XlYuaIAQUdLFwBAE5cqVSwWYv/zyS+DEIgnQJTsbNKMa2S44hmyv4bhi0aJF6vR2UIYSh0KFCqlspgTZ8rrIaX/JxG7evDlaXq+vfX0/+VuVAC40EqRKQCblHJKdNJCSAukyEPT3IceRsgR5LeU1l4l3EvDKftk2+PDhg/rXENBnz55dTZaSLxthPa+IlBmk/PzF49WrVypgN5Dt0MpghNxWvgQEJX8zUvIRUfL6yCS9mMRgNgbJt6wtx0ri5fsagPz+/zwU5u07YKr699hpFxxD2LfdVKQwhjx/jLf+/nj93x4sDPItlIiIoofhA1zezw1ZOcleSlYvbdq0ana3BB5yGlZOfxtO50sbrXLlyoV6XAl+JLsoF6nplPIFOT0uQZ0ES5IRDYu0a5LT4m3bto30c5OSgixZsqhVIeVx5SJBY2hZP8maSh2rBEKG09byvL9HbiunmuXUtQRYoZGgT2pb5dKgQQOVhZVASEovwnq9vv59GeptDdlZKWeQwPHr4CwiDIGfHMvw89u3b/HXX38F1vIayO9OyiX+++8/9RzkdZMstGTTg45XsuSGIFZIna3UDktpS69evb4ZgzwHyTBHpMwgU6ZMKqCVvxXDuN3c3FTNs3yRCY2cfZC/haDkbzpo+Ud4SUZXMrsxicFsDPLz0eHl+yTRekx/nT/2HZuJHU8eqGmu2W3tMCgK7VocChWKttNxRESmSgLQrwME6Z8pWUA5xd2jRw90795dfcBL/afUEkqWTYJaOe3av39/FXRJTaVcL9eFNmdBaiwl4JEspbSUWr16tQrWDIGCnAI/fPiwqr+UU9uSFfyaPIbU6EowKreTYEkmgEWkp6cEezKJTGp/ZSKU/Cun9yUol2BSnoOUHkgwIkuhy+lzeTx5vtLuSSaADRs2TB3re/MzJEspNZeShZQAz9vbW/UslTINeS2nT5+usoES9Mjjbtq0SQVhErx97/UKSrK50g9Vgmb5vcnrIr/D8uXLhzh5K7wksyiBqGRODUGhZLTlb0SyyF8/fyk7kKytPFcJdKVkol27dqoUUMol5O9I6oolcJfJVUKen9QnS82tTDyU3418Cbh79646vS8BsgS5ESkzsLCwUI8jvz+pSZbgdvjw4eq4QXsry9+SPJ78jQv5glOqVClVZiDPT+bkLFy4UF0M5IuG1DNLDbkwBL/yewtaiiKZ5IiWXESY3szEZmsun09+ga20Pv49RW1/ffng7qavvLaqvsCSQvpF55aEeBvD5cqFK/pCDk6B7Vo6tmun93BxibX2NUREoQmrxY6xk9ZcX7fDkkv79u0j3ZqrWLFi+kGDBoXYPkpaMUkLJ2kJJe2jSpQood+7d2/gbU+cOKH/4YcfVPuksFpz/fnnn/oCBQqocTk7O+vr1asX6nMMrT2UPJY8hrSFErt379aXKlVKtZqS8cnzWLhw4TetueQxpc3Wjh071P3lft9r0yQtqwzjTZw4sWrdtWXLFnWdPIZcJ6+HPG6lSpX058+fD9frFdnWXEHJ/eU4YZk/f756bIN8+fLpu3btGuJtN2zYoJ7nmzdvAttq9ezZU58lSxb12mbLlk0/YMAAvbu7e4j3lddGxi7PQf4WxowZE6nWXEI+54cPH65PkSKF+puS1/bWrVv6oOS5y+sSlPxu8+bNq+4jv+ugfweGv6mQ/r8Jepzjx4/rEyVKpPf09NTHZGsuC/kPzIik1+WboXwLl1MaMcnX2x8LewWUC3SqfwY2Vb79xjz7/GwsurII6eKnw7Y622Br9aUWJyjJGMg3J/km5GhhiXmLFqFN+3YxOn4iovCSSUKGGdzmvsqg1EdKtk2ycDLpKS6TkgvJGEr2ULK2cZlMApOSgQ0bNqhJgfR9knmWLhvSESGi7xsRiddYZqChJ+5PsOLaCvVzvyL9Qg1kDTU1KVOkRIqPH/Fb6jQoW79BLI6UiIhCIz1Tpf+szOyXD15ZelRIv9O4RnrTSnsvOWUtAayc9pb6yrgeyAo5vS9tvmSpePo+mSwnJR9SshDTGMxqaPrZ6fDR+aBEqhKomK7iN9fLsoNSxC31Q1In9OfGTfhU73+wCzIbkoiItDdt2jRVMyiTtwoXLqzqBEOqdTV1UicrdblSKynPT2pUJQNtLipUqKD1EEyGra1tYE11TGMwq5FTL05h7+O9sLKwwoCiA74pHpfVN6TvnhSCywovQmbOPmEgS0RkVGTS0rlz2vTTjW2yQphciIwJIyMN+On8MOn0JPVzoxyNkC1xtmBpeZnZKaenZJanzDCV2ZhERERE9C0GsxrYfHsz7n64i4R2CdGtQLfA/VIELethyxrUQtppSFPoiKwKQ0RERGROGCXFMldvV8y9OFf9LIGsBLRiy5YtqgedTB4w9NWLi5MHiIiIiKITg9lYNv/ifBXQZk2UFQ2zN1T7pOGwrPwhTaRLlCihVhOJzCobREREROaGwWwsuvv+Ljbc2qB+HlhsIKwtA15+WYlDViqRdbJltY3IrjVNREREZG4YzMYSWZtiypkp8Nf748d0P+LxkcewymSFokWLquu7dOmi9RCJiIiITA4ngMWSI17PceLFCVj5WeHtmrdqVQy5SI0sERHR8OHD0alTJ62HYRKk80/GjBlx9uxZrYdCRsAogtl58+apP0pZyqx48eI4ffp0mLfftGkTcubMqW4vq0v8/fffMHYzXS/C+4U33kx+gzVL16i+sk2bNoWTk5PWQyMiMmvS01vek0M6Q9atWzd1ndwm6O3r1q0b6vHk80zuIxd5jy9UqJD63ArLy5cvMWvWrMC+4kGdOHECVlZWqFGjxjfXHTx4UD3Ohw8fQhyHlLAFdeDAAfz8889ImjSpWownd+7cqp/5s2fPEFuf4ULGK69tqlSpYPf/9u4EOqryigP4JQlZkCSAGEmQIAQTOCAFiewclmqDoIYWhBaKqGAoAaRYtiI2CAYoUqhFZBEBFQoFCugBBEFAtrpUBC07hvWgIJWSSILZvp7/9bw5kzDZk5l5yf93ziOZmTfzvjcfmXfnvvt9LyBAr3LpfCx3fg+dFzzHmpB/3LhxegEHIo8Hs7jGMeZVTUpKkkOHDuk1fOPi4uTq1asu1z948KAGgbjeNS4hiA8ULP/5z3/Em32574KkTE2Ry6cvy1133SXbtm2T5ORkTrtFROQFGjRooINvMzIy8lw3/u9//7tERkaW+PVwSVtcxRHHKZST4Uwcjl8FWbp0qXTs2NHl4N8333xTRo8eLXv37tUBw6W1ePFivWJXvXr15J///KccO3ZMFi1apGcIS3sVr5Iew62s6sMPPyznzp2T9evX65XT3njjDalfv75jnc8++0zfP2vZsWOH3v/EEz8NnIZBgwbJ/v375ejRo6VqO1UixsPatm1rRo4c6bidk5NjIiIizMyZM12u379/f9O7d+8897Vr184MHz68WNu7ceOGwW7jZ0W7lZ5p5g1733SI6anbxNKlcxdz+vhJk3b9RomXK+cvm2MxTXXBbSIib5GRkWGOHTumPy25ubkm81a2RxZsu7iGDBli4uPjTYsWLczKlSsd969atcq0bNlSH8M6+dcvSMOGDc28efMct7OyskyNGjXMpEmTCnxO8+bNzWuvvXbb/WlpaaZmzZrmxIkTZsCAASY5OTnP47t379Zjy/Xr1wttx8WLF42/v7/5/e9/73L7rp5fEcdwWLhwoWncuLHJzMws9nbGjBljoqKibuvX7t27mylTppSq7eSdnxulidc8mhbEtzNcAvCPf/yj4z4fHx/95ojTKq7gfnwLdIZvgZs2bXK5Pqa7wmJJTU0Vd0lP+0F8ffwkNeO64GK1I+68U0ZcvSpZfeLlottaQUTkGdmZubJkzEce2XbCq12leoBviZ6Dub6XL1+uGT9YtmyZPP3003oqvyxwBg6z1OCY58r333+vWdLY2NjbHlu7dq2W1cXExMhvf/tbvZgOjpn5L4FeFJQ5YPsTJkxw+TjmN4cLFy5o6UFhJk+erEtpjuHW5do7dOigJQPvvvuunq3E9JQoGUA5RX7YzsqVK/XYn3+/27ZtK/v27Sty/6ly82gwe+3aNcnJyZG77747z/24feLEiQLrilytj/tdmTlzprz00kviKT7VfGRwt4kStmectA/yL5fXPB/eRGJCapbLaxER0U8QLCIwO3/+vN4+cOCAlh6UJZhFIIZT+DiV36NHD5frIIDEjDeYptFViQHaBT179tTXwZUhu3XrVqJ2nD59WkJCQrRGtTBow+HDhwtdp06dOqU+hkNKSors2rVLvzSgTvbMmTOSmJgoWVlZWq6QH5JVqLF1rlt2bq/VX1R1VfqCTXwwOWdykZlFbZQ7hNwZIgOntZas3CwJCjhU4m/SBUEgi2+/RETezM/fRzOkntp2SSFDiEFWuAIjgkv8Xrdu3VJtH1nGKVOmaN1tzZo1ZdasWS4HcIFVp4sBVM5QS4rBVBs3bvxpn/z8tPYWAW5Jg1nsT3GOQdhGkyZNpCLl5uZKWFiYLFmyRDOxbdq00QFor7zyistgFvv7yCOPuAz2g4KCJD09vULbS97Po8EsPiTwH/nKlSt57sdtFKi7gvtLsj5GSWLxBOxb7bDaHtk2EZGnIXgq6al+T0OpwahRoxyj9Etr/PjxmklEIItMZWGBpBUwX79+XQNq5yAuOzs7TxCHoBTHtNdee01CQ0M12wrWpdCdIZuJdQCzBWAdDKYqLDtbkjKD0hzDAdtH2YVzSUGzZs30DCsy2ZipwIKs686dO/WS7wWVaDi/Z1Q1eTS9h/+w+Eb24Ycf5vnGhtuop3EF9zuvDxjlWND6RERExYVT+QiocMob4zFKC4EeMpwI6orKiEZFRWlQirpZC4LYt99+W0sUcNrfWo4cOaLB7erVq3W9++67T8/UoXY1/6l8BK8IYqFfv356zJ09e7bLNlhTe1llBoUt1hRmpTmGQ6dOnbS0AOtaTp06pUGucyALqGFGFregrDZmMmrdunUh7y5VBR4vM0AJwJAhQ7TwHYXcmBPv5s2bWnQPTz75pE7XgdpXGDNmjHTt2lX/wPGfG/VMmDQZpyuIiIjKAtnC48ePO34vCALF/LWlmLu1NGVs1qApTDNlzV+7efNmzdRiGkoru2rp27evZm0RVAYHB8uwYcN0rliUCGDu9YsXL2qZQ/v27XW6L0C75s2bp1lnlNvh2Iq5XC9duqRBMzLIOK6WtMygqGO4q+P4iBEjNLOM4zmmHEM9Ly7l/txzz+V5bQS7CGbx+gVNY4nBX9OnTy/Bu02VkvEC8+fPN5GRkTptCKb5+Pjjjx2Pde3aNc+UKLB27VoTHR2t62M6ky1bthR7W+6cmouIqKoobIodb1fUVFuupuayplt0XoYOHepyaq7i2Lp1q6lfv75ObQWPPvqo6dWrl8t1P/nkE93ekSNH9Dbe86SkJNO0aVMTFBRkGjVqZBISEsx3331323N37Nhh4uLiTO3atU1gYKA+Z9y4ceby5cumIo7hBR3HDx48qNNqBgQE6DRdmHIsOzs7zzrbt2/X/Tx58qTL7eI1atWqZdLT00vddqocU3NVwz9SheAbKb7l4lu1VWtERERlg4FOZ8+elUaNGt02kImKhkMxrp41duxYvTAQFQ2D4XCRBtTvUuX73ChJvMYh8URERB6GulqUy6FWloqGumaUVCD4J/J4zSwRERGJtGrVShcqGgaKYeozImBmloiIiIhsi8EsEREREdkWg1kiIio3VWxMMRF5wecFg1kiIiozXNEJeGlRIirJQL6i5nQuDg4AIyKiMsPBCJdTvXr1qt6uUaNGkVe+IqKqKzc3V7777jv9rCjoohjFxWCWiIjKBS7dClZAS0RU1NXvIiMjy/zFl8EsERGVCxyQwsPDJSwsTLKysjzdHCKywRRrCGjLisEsERGVe8lBWWvgiIiKiwPAiIiIiMi2GMwSERERkW0xmCUiIiIi2/KrqhP0pqameropREREROSCFacV58IKVS6YTUtL058NGjTwdFOIiIiIqIi4LTQ0tLBVpJqpYtcexCS9ly9fluDgYLdM6I1vFgicL168KCEhIRW+PSp/7EP7Yx/aH/vQ3th/9pfq5j5EeIpANiIiosjpu6pcZhZvyD333OP27aLj+Qdsb+xD+2Mf2h/70N7Yf/YX4sY+LCoja+EAMCIiIiKyLQazRERERGRbDGYrWEBAgCQlJelPsif2of2xD+2PfWhv7D/7C/DiPqxyA8CIiIiIqPJgZpaIiIiIbIvBLBERERHZFoNZIiIiIrItBrNEREREZFsMZsvBggUL5N5775XAwEBp166dfPrpp4Wuv27dOmnatKmuf//998vWrVvd1lYqex++8cYb0qVLF6ldu7YuDz30UJF9Tt73d2hZs2aNXg2wT58+Fd5GKt8+/N///icjR46U8PBwHWEdHR3Nz1Mb9d9f//pXiYmJkaCgIL2y1NixY+XWrVtuay/ltXfvXnnsscf0ilv4TNy0aZMUZc+ePfLAAw/o31+TJk1kxYoV4hGYzYBKb82aNcbf398sW7bMHD161Dz77LOmVq1a5sqVKy7XP3DggPH19TWzZ882x44dM1OmTDHVq1c3X331ldvbTqXrw4EDB5oFCxaYL774whw/ftw89dRTJjQ01Fy6dMntbafS9aHl7Nmzpn79+qZLly4mPj7ebe2lsvfhjz/+aGJjY02vXr3M/v37tS/37NljDh8+7Pa2U8n7b9WqVSYgIEB/ou+2b99uwsPDzdixY93edvrJ1q1bzQsvvGA2bNiAWa7Mxo0bTWFSUlJMjRo1zPPPP6/xzPz58zW+2bZtm3E3BrNl1LZtWzNy5EjH7ZycHBMREWFmzpzpcv3+/fub3r1757mvXbt2Zvjw4RXeViqfPswvOzvbBAcHm7feeqsCW0nl3Yfot44dO5qlS5eaIUOGMJi1WR8uXLjQNG7c2GRmZrqxlVRe/Yd1e/Tokec+BEWdOnWq8LZS0YoTzE6YMME0b948z30DBgwwcXFxxt1YZlAGmZmZ8vnnn+tpZouPj4/e/te//uXyObjfeX2Ii4srcH3yvj7MLz09XbKysqROnToV2FIq7z6cNm2ahIWFydChQ93UUirPPnzvvfekQ4cOWmZw9913S4sWLWTGjBmSk5PjxpZTafuvY8eO+hyrFCElJUVLRHr16uW2dlPZeFM84+f2LVYi165d0w9OfJA6w+0TJ064fM63337rcn3cT/bow/wmTpyoNUb5/6jJe/tw//798uabb8rhw4fd1Eoq7z5E8LNr1y4ZNGiQBkFnzpyRxMRE/WKJqxSRd/ffwIED9XmdO3fGGWLJzs6W3/3udzJ58mQ3tZrKqqB4JjU1VTIyMrQW2l2YmSUqg1mzZukAoo0bN+qgB/J+aWlpMnjwYB3IV7duXU83h0opNzdXM+tLliyRNm3ayIABA+SFF16QRYsWebppVAwYOIRM+uuvvy6HDh2SDRs2yJYtW2T69OmebhrZEDOzZYADoa+vr1y5ciXP/bhdr149l8/B/SVZn7yvDy1z5szRYHbnzp3SsmXLCm4plVcffv3113Lu3DkdtescGIGfn5+cPHlSoqKi3NByKsvfIWYwqF69uj7P0qxZM80W4bS3v79/hbebSt9/L774on6pHDZsmN7GzD43b96UhIQE/VKCMgXybvUKiGdCQkLcmpUF/m8pA3xYIiPw4Ycf5jko4jZquVzB/c7rw44dOwpcn7yvD2H27NmaQdi2bZvExsa6qbVUHn2IafG++uorLTGwlscff1y6d++uv2OKIPL+v8NOnTppaYH1RQROnTqlQS4DWe/vP4w1yB+wWl9Mfhp/RN6ugzfFM24fclYJpyPB9CIrVqzQqSkSEhJ0OpJvv/1WHx88eLCZNGlSnqm5/Pz8zJw5c3Rap6SkJE7NZbM+nDVrlk5Bs379evPNN984lrS0NA/uRdVW0j7Mj7MZ2K8PL1y4oLOIjBo1ypw8edJs3rzZhIWFmZdfftmDe1F1lbT/cOxD/61evVqnePrggw9MVFSUzvhDnpGWlqZTTmJBeDh37lz9/fz58/o4+g/9mH9qrvHjx2s8gykrOTWXjWFutcjISA1wMD3Jxx9/7Hisa9eueqB0tnbtWhMdHa3rY1qLLVu2eKDVVNo+bNiwof6h51/w4Uz2+Tt0xmDWnn148OBBndoQQRSm6UpOTtYp18j7+y8rK8tMnTpVA9jAwEDToEEDk5iYaK5fv+6h1tPu3btdHtusfsNP9GP+57Rq1Ur7HH+Dy5cv90jbq+Ef9+eDiYiIiIjKjjWzRERERGRbDGaJiIiIyLYYzBIRERGRbTGYJSIiIiLbYjBLRERERLbFYJaIiIiIbIvBLBERERHZFoNZIiIiIrItBrNERCKyYsUKqVWrlthVtWrVZNOmTYWu89RTT0mfPn3c1iYiIndgMEtElQaCNQR1+ZczZ854RbBstcfHx0fuueceefrpp+Xq1avl8vrffPONPPLII/r7uXPndDuHDx/Os86rr76q7ahIU6dOdeynr6+vNGjQQBISEuT7778v0esw8Cai4vIr9ppERDbQs2dPWb58eZ777rrrLvEGISEhcvLkScnNzZUjR45oMHv58mXZvn17mV+7Xr16Ra4TGhoq7tC8eXPZuXOn5OTkyPHjx+WZZ56RGzduyD/+8Q+3bJ+IqhZmZomoUgkICNDAznlBhnDu3Lly//33yx133KHZwsTERPnhhx8KfB0Em927d5fg4GANQtu0aSP//ve/HY/v379funTpIkFBQfp6zz33nNy8ebPQtiFbifZERERoFhXPQdCXkZGhAe60adM0Y4t9aNWqlWzbts3x3MzMTBk1apSEh4dLYGCgNGzYUGbOnOmyzKBRo0b6s3Xr1np/t27dbst2LlmyRNuB7TqLj4/X4NPy7rvvygMPPKDbbNy4sbz00kuSnZ1d6H76+fnpftavX18eeugheeKJJ2THjh2OxxHkDh06VNuJ9y8mJkazxs7Z3bfeeku3bWV59+zZo49dvHhR+vfvryUhderU0fYiE01EVReDWSKqEnBq/29/+5scPXpUA6Vdu3bJhAkTClx/0KBBGlh+9tln8vnnn8ukSZOkevXq+tjXX3+tGeC+ffvKl19+qRlHBLcINksCgRyCSQSHCOb+8pe/yJw5c/Q14+Li5PHHH5fTp0/rumj7e++9J2vXrtXs7qpVq+Tee+91+bqffvqp/kSgjPKDDRs23LYOAsz//ve/snv3bsd9KAVAAI19h3379smTTz4pY8aMkWPHjsnixYu1TCE5ObnY+4hAE5lnf39/x33YZ7y369at09f905/+JJMnT9Z9g3HjxmnAivcY7cfSsWNHycrK0vcFXzDQtgMHDkjNmjV1PQT7RFRFGSKiSmLIkCHG19fX3HHHHY6lX79+Ltddt26dufPOOx23ly9fbkJDQx23g4ODzYoVK1w+d+jQoSYhISHPffv27TM+Pj4mIyPD5XPyv/6pU6dMdHS0iY2N1dsREREmOTk5z3MefPBBk5iYqL+PHj3a9OjRw+Tm5rp8fXycb9y4UX8/e/as3v7iiy9ue3/i4+Mdt/H7M88847i9ePFibUdOTo7e/vnPf25mzJiR5zXeeecdEx4ebgqSlJSk7wPe+8DAQG0Hlrlz55rCjBw50vTt27fAtlrbjomJyfMe/PjjjyYoKMhs37690NcnosqLNbNEVKmgNGDhwoWO2ygrsLKUOC1/4sQJSU1N1WzorVu3JD09XWrUqHHb6zz//PMybNgweeeddxynyqOiohwlCMieIjtqQTyJjOPZs2elWbNmLtuGulFkErEett25c2dZunSptge1s506dcqzPm5jW1aJwMMPP6yn5JGJfPTRR+UXv/hFmd4rZGCfffZZef3117W0Afvz61//WrPY1n4i++mciUWJQGHvG6CNyCJjvZUrV+pAtNGjR+dZZ8GCBbJs2TK5cOGCllkgs4rSisKgPRjMh8ysM2wH2XIiqpoYzBJRpYLgtUmTJred6kbwN2LECA3MUGuJsgDUbSKIchWUoW5z4MCBsmXLFnn//fclKSlJ1qxZI7/85S+11nb48OFa85pfZGRkgW1DEHbo0CENFlH7ijIDQDBbFNStIlBGWxCY4zQ8guz169dLaT322GMahGMfH3zwQT11P2/ePMfj2E/UyP7qV7+67bmooS0ISgqsPpg1a5b07t1bX2f69Ol6H95HlBKgrKJDhw76vrzyyivyySefFNpetAe1y85fIrxtkB8RuR+DWSKq9FDzimwogicr62jVZxYmOjpal7Fjx8pvfvMbnSUBwSwCS9R65g+ai4Jtu3oOBphhMBayoF27dnXcj9tt27bNs96AAQN06devn2ZoUeeK4NyZVZ+KLGphEJAiUEVwiIwnMqrYNwt+R31uSfczvylTpkiPHj30y4S1n6iBxSA8S/7MKvYhf/vRHtQnh4WF6XtBRAQcAEZElR6CMQwemj9/vqSkpGjpwKJFiwpcH6e9MZgLI+jPnz+vwRcGglnlAxMnTpSDBw/qOjiFjkFaGHlf0gFgzsaPHy9//vOfNVhDAIkBZ3htDL4CzMawevVqLZM4deqUDp7CjAGuLvSAYA9ZXwzmunLlipY3FFZqgMwsTvlbA78sGJj19ttva1YVA+cwzRayqghOSwLZ15YtW8qMGTP09n333aczQ2BgGPblxRdf1PfXGQa3oZQD78W1a9e0/9C+unXr6gwGyCIjU40+Qob80qVLJWoTEVUeDGaJqNL72c9+psEggsUWLVpoJtJ5Wqv8MJUXRvpjJD8yszilj6m0ENQBArOPPvpIAzFMz4UpsBD4IetYWgjIUKf7hz/8QacQQyCKulMEfoBT8bNnz5bY2FgtCUDpxNatWx2Z5vxTY2H2A8w+gDYh+CsIMqbI7CJoRFmFM8wcsHnzZvnggw90m+3bt9cyBEwLVlLIbqM+GFNroUQDGWFkmNu1a6fvtXOWFlDLi0wx9hclBPhCgXKQvXv3aikHno8vFygVQc0sM7VEVVc1jALzdCOIiIiIiEqDmVkiIiIisi0Gs0RERERkWwxmiYiIiMi2GMwSERERkW0xmCUiIiIi22IwS0RERES2xWCWiIiIiGyLwSwRERER2RaDWSIiIiKyLQazRERERGRbDGaJiIiISOzq/4C9HuvOGYO+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "for i, (name, model) in enumerate(models.items()):\n",
    "    y_pred = model.predict(X_test)\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    plt.subplot(1, len(models), i+1)\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.title(name)\n",
    "    plt.xlabel('预测')\n",
    "    plt.ylabel('真实')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plt.figure(figsize=(8, 6))\n",
    "for name, model in models.items():\n",
    "    if hasattr(model, \"predict_proba\"):\n",
    "        y_score = model.predict_proba(X_test)[:, 1]\n",
    "    else:\n",
    "        # SVM等没有predict_proba时用decision_function\n",
    "        y_score = model.decision_function(X_test)\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, label=f'{name} (AUC={roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC曲线')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb402b7",
   "metadata": {},
   "source": [
    "## 5. 总结与展望\n",
    "\n",
    "- 本项目实现了ASD/TD病例的多种分类算法建模与评估。\n",
    "- 结果显示不同模型在准确率、召回率等指标上的表现差异。\n",
    "- 后续可尝试：\n",
    "    - 更丰富的特征工程（如统计特征、时序特征等）\n",
    "    - 深度学习方法（如LSTM、CNN等）\n",
    "    - 增加数据量或数据增强\n",
    "    - 进一步分析误判样本，优化模型"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
